Data & Analytics
ETL Development Services
We build and operate the pipelines that move your data (extracting it, cleaning it and loading it into the warehouse), reliably, on schedule, and with the monitoring that stops them corrupting your data in silence.
What ETL Development means in practice
Who it’s for: Teams who are wrangling data by hand, fighting feeds that break without warning, or trying to consolidate several source systems into one trustworthy place, and who need the movement automated, reliable and monitored rather than held together by a fragile script.
This is the delivery service for the plumbing of a data platform: the pipelines that take data out of the systems where it is created, reshape and clean it into something consistent, and load it into a warehouse or downstream system on a dependable schedule. It is the least glamorous part of any data project and, not coincidentally, the part where those projects most often break. A dashboard is only as trustworthy as the pipeline feeding it, and a pipeline that runs quietly wrong is worse than one that has visibly stopped, because everyone keeps making decisions on numbers that drifted out of true weeks ago and nobody noticed.
We build both classic ETL (transform the data on the way in and load the finished result), and the modern ELT pattern, where you land the raw data in the warehouse first and transform it there with a tool like dbt, keeping the untouched source as the thing you can always recompute from. Which one is right depends on your warehouse, your volumes and how often the transformation logic changes, and we will make that call on your situation rather than default to whichever is fashionable. Either way the pipeline is orchestrated properly (Airflow, Dagster or a cloud-native scheduler), with the dependencies, retries and alerting made explicit rather than left to a cron job and hope.
The distinction worth being clear about is that this service is the movement, not the destination or the scale. Data Warehouse Development is about the place the data lands and how it is modelled for analysis; Big Data is about the techniques that apply once volume or velocity breaks the ordinary tools. ETL development is the connective tissue between your source systems and that destination, and the hard parts of it are not the happy path where everything arrives clean and on time. The hard parts are data quality, schema changes upstream that nobody warned you about, late-arriving records, and the reliability and observability that decide whether you find out about a broken load from your monitoring or from a confused executive three days later.
What you get
- Pipelines built to extract from your real source systems (databases, APIs, third-party services, files), and load into your warehouse or downstream target, on a schedule you can depend on
- The ETL-versus-ELT decision made explicitly for your case, with raw data landed and preserved where the ELT pattern fits, so a transformation mistake is a re-run rather than a data-loss event
- Orchestration in Airflow, Dagster or a cloud-native scheduler, dependencies, retries, backfills and idempotency designed in, not a cron line that silently overlaps itself
- Incremental loads and, where the source supports it, change data capture, so you move only what changed instead of reprocessing everything every night as volumes grow
- Data quality checks that run in the pipeline (freshness, row counts, nulls, uniqueness, referential and business-rule tests), that fail loudly and stop bad data propagating downstream
- Monitoring and alerting on the things that actually predict trouble: failed and late runs, volume anomalies, schema drift and stale data, routed to somewhere a human will see it
- Documentation and handover: the lineage, the schedule, the recovery procedure and the runbook, so the pipeline is something your team can operate and extend, not one person’s private knowledge
What ETL Development does for you
Time back, and errors removed with it
Manual data wrangling is expensive twice over: it consumes hours of skilled people’s time, and every hand-run export, filter and paste is a chance to introduce a mistake nobody catches. Automating the movement gives the time back and takes the human error out at the same time. The pipeline does the same correct thing every night whether or not anyone is watching, and the person who used to babysit the spreadsheet gets to do the work you actually hired them for.
Trust in the numbers, because the pipeline is watched
The single most important thing a pipeline can do after moving data correctly is tell you loudly when it has not. A pipeline with no monitoring does not fail safely: it fails silently, and everyone keeps deciding on data that quietly went stale or wrong. We build the checks and the alerting in, so a failed load, a late feed, a sudden drop in row count or a changed schema surfaces to a human immediately. That observability is what lets people trust the reports on top, because the thing feeding them is watched rather than assumed.
A platform that scales with your data, not your bill
A naive pipeline reprocesses everything every run, which is fine at a thousand rows and ruinous at a hundred million. The nightly job slowly creeps from minutes to hours until it no longer finishes before people need the data. Incremental loading and change data capture mean the pipeline moves only what actually changed, so the cost and the runtime track the volume of change rather than the total size of the dataset. That is the difference between a platform that keeps working as you grow and one you have to rescue every time the data does.
Why teams choose us for ETL Development
- You are tired of the pipeline being the part that breaks. The fragile script, the cron job that silently overlaps, the load that fails at 2am and is discovered at 11am, and you want it built by people who treat reliability and observability as the actual job, not an afterthought bolted on at the end.
- You want an honest recommendation on ETL versus ELT and on the orchestration tool, argued from your warehouse, your volumes and how often your logic changes, not a default template applied because it is what the last three clients got.
- You need the data quality problem taken seriously: checks that run in the pipeline and stop bad data propagating, rather than a load that cheerfully imports whatever the source sent and leaves you to discover the corruption in a dashboard weeks later.
- You care about being able to operate and extend the pipeline yourself afterwards, lineage you can read, backfills that are a supported command rather than a panicked manual fix, and a runbook that means an on-call engineer who did not build it can still recover it at 3am.
What ETL Development includes
The concrete pieces of work this covers, scoped to what your problem actually needs.
Extraction from source systems
Getting data out cleanly and without hammering the systems it lives in: incremental reads from operational databases, paginated and rate-limited pulls from REST and other APIs, ingestion from third-party SaaS tools, and file-based feeds from SFTP, object storage or partners. We handle the awkward realities, authentication that expires, APIs that throttle, sources that are only available in a nightly window, and we extract in a way that does not put analytical load on the production systems your business runs on.
Transformation, cleaning and modelling
Turning raw, inconsistent source data into something consistent and analysable: type casting and standardisation, deduplication, reconciling the same entity represented differently across systems, handling nulls and bad values deliberately rather than letting them poison downstream numbers, and applying the business logic that turns operational records into the metrics people actually ask for. In an ELT setup this is where dbt earns its place, transformations as version-controlled, tested SQL that run in the warehouse and are reproducible from the preserved raw data.
Orchestration and scheduling
The layer that runs the pipeline reliably and in the right order: Airflow, Dagster or a cloud-native scheduler, with the dependencies between steps made explicit so a downstream transform never runs on data the upstream extract failed to load. Retries with sensible backoff, alerting on failure, backfills as a first-class operation, and idempotent tasks so a re-run is safe. This replaces the cron-job-and-hope arrangement where jobs silently overlap, fail without telling anyone, and cannot be safely re-run when they do.
Incremental loads and change data capture
Moving only what changed instead of reprocessing the whole dataset every run. For sources with reliable timestamps or sequence keys, incremental extraction based on a watermark; for databases that support it, change data capture reading the transaction log to stream inserts, updates and deletes as they happen. This keeps the pipeline affordable and fast as data grows, and it is the difference between a nightly job that finishes in minutes and one that eventually stops finishing at all.
Data quality and testing
Checks that run inside the pipeline and fail it loudly when something is wrong, rather than importing bad data and leaving you to find it in a report. Freshness tests that catch a stale source, row-count and volume-anomaly checks that catch a partial load, null, uniqueness and referential tests that catch structural problems, and business-rule assertions that catch the data that is technically valid but obviously wrong. A failed check stops the bad data propagating downstream and raises an alert, so the corruption is contained at the boundary.
Monitoring, alerting and recovery
The observability that turns a pipeline from a black box into something operable: monitoring on run status, duration, data freshness, row volumes and schema drift, with alerts routed to somewhere a human will actually see them. Clear lineage so when a number looks wrong you can trace it back through the transforms to the source. And a genuine recovery story, how to re-run a failed load, how to backfill a range, how to handle late-arriving data, written down so the pipeline can be rescued by whoever is on call, not only by the person who built it.
Where it fits
Replacing a manual data-wrangling routine
Someone exports data from three systems every Monday, cleans it in a spreadsheet, reconciles it by hand and uploads the result. A job that eats a morning and quietly introduces errors. We turn that routine into a scheduled, tested pipeline: the same extraction, the same cleaning and reconciliation, done automatically and identically every time, with quality checks that catch the problems the human used to catch by eye. The morning comes back, and the copy-paste mistakes go away with it.
Stabilising an unreliable data feed
A feed you depend on (a partner API, a nightly export, a third-party sync), breaks or arrives late without warning, and you find out from a broken report. We rebuild it to be resilient: retries and sensible handling when the source is down, idempotent loads so a retry is safe, checks for freshness and completeness, and alerting that tells a human the moment the feed fails or is late. The feed stops being a recurring fire drill and becomes something that either works or tells you promptly that it did not.
Consolidating several sources into one view
The same information lives in a CRM, a billing system, a product database and a few spreadsheets, and every attempt to get a single answer means reconciling them by hand. We build the pipelines that pull from each source, reconcile the different representations of the same entity, resolve the conflicts deliberately, and land one consistent dataset in the warehouse. The result is a single place people can trust, so the arguments about whose system is right stop and the decisions start.
Feeding a new warehouse or analytics platform
You have stood up (or are standing up), a warehouse, and now it needs the pipelines that actually keep it fed on a schedule. We build the ingestion and transformation layer that populates it: raw landing where the ELT pattern fits, dbt models for the transforms, orchestration that runs it in order, incremental loading so it stays affordable, and monitoring so a stale table is caught before anyone reports on it. The proof-of-concept load that ran once by hand becomes a production pipeline the platform can rely on.
How we approach ETL Development
We start from the assumption that the pipeline will be fed bad data, that the schema upstream will change without notice, and that a source will occasionally be late or down, because over a long enough horizon all of these happen. So the design question is never just “how do I move this data on a good day”, it is “what happens when the source sends a malformed row, or adds a column, or is unreachable at 2am, and how do we notice within minutes rather than days”. That framing is the difference between a pipeline that runs for years and a script that limps along until it quietly corrupts something.
Concretely, that means we build for idempotency and recoverability from the start: a run that fails halfway and is retried produces the same correct result rather than duplicated or half-loaded data, and a backfill of last month is a supported operation, not a frightening manual intervention. We put data quality checks between the stages so bad data is caught at the boundary rather than discovered in a report, we prefer incremental loads and change data capture so the pipeline stays affordable as volume grows, and we wire in monitoring as part of the build, because a pipeline with no observability is not finished, it is just untested in production.
How the engagement runs
We start with the sources and the failure modes, not the tooling. The first questions are what systems the data comes from, how each one behaves: how it authenticates, how it is read incrementally, whether it warns you before it changes shape, how often the data is needed, and, critically, what happens when each source is late, down or sending something malformed. Those answers shape everything: the ETL-versus-ELT choice, the orchestration, the schedule, and where the quality checks need to sit. We map the current state honestly, including the fragile manual steps and the feeds that already break, so we are designing around the real behaviour of your sources rather than the documented one.
From there we build in reviewable increments, one source or one pipeline at a time, proving each against real data before moving on. Idempotency, incremental loading and the quality checks go in from the first pipeline, not retrofitted at the end. We run it against realistic volumes early to find where it strains, rather than discovering at scale that it was built for a sample. Monitoring and alerting are part of the build, not a final task: a pipeline is not considered done until it can tell you when it has failed. You see progress in working, monitored pipelines and in the manual routines they retire, and we hand over the lineage, the runbook and the recovery procedures so your team can operate it without us.
How we architect it
The first architectural decision is batch versus streaming, made on how fresh the data genuinely needs to be rather than on instinct. For the great majority of analytical needs, scheduled batch (hourly, nightly, or on whatever cadence the decisions actually require), is simpler, cheaper and easier to reason about, and we default to it unless there is a real requirement for lower latency. Streaming, with change data capture off the source database and a processing layer that handles events continuously, is the right answer when the business genuinely needs data within seconds rather than hours; it is a meaningfully more complex thing to build and operate, so we reach for it when the requirement warrants the cost, not because real-time sounds better on a diagram.
The second is where the transformation happens. The modern ELT pattern: land the raw source data in the warehouse untouched, then transform it there with dbt, has become the sensible default for warehouse-centric work, because it separates ingestion from logic, keeps an immutable raw copy you can always recompute from, and makes the transformations version-controlled, tested SQL rather than opaque code buried in a pipeline. When the transformation logic changes, or a bug is found, you fix the model and rebuild from the raw data rather than needing to re-extract from the source. Classic ETL (transforming before load), still earns its place where the warehouse should not hold raw data for privacy or volume reasons, or where the transformation is heavy and better done in a dedicated processing layer. Throughout, we design for idempotency and clear lineage, so any stage can be safely re-run and any number can be traced back to where it came from.
Security
A pipeline is data in motion, which makes it one of the places sensitive data is most exposed, so we treat it accordingly. Data is encrypted in transit on every hop: the pull from the source, the write to intermediate storage, the load into the warehouse, and encrypted at rest wherever it lands, including the raw landing zone that the ELT pattern deliberately keeps. Credentials and connection strings for every source and target are held in a secrets manager and kept out of code, configuration files and logs, because a pipeline that logs a connection string has quietly leaked the keys to the systems it touches.
Personally identifiable information gets particular care, because a pipeline can silently spread PII into every downstream system it feeds. We identify it early, and depending on the requirement we mask, tokenise or hash it as it moves, minimise it so downstream systems only receive the fields they genuinely need, and avoid retaining it in intermediate stages longer than the pipeline actually requires, with an eye to the obligations that come with handling personal data. Access to the sources and the warehouse is least-privilege, with the pipeline’s credentials scoped to exactly what it reads and writes and nothing more, so a compromised pipeline credential reaches as little as possible. And because the monitoring already watches the data flowing through, it is also positioned to surface the unusual access or volume patterns that can be the first sign of something wrong.
Signs it’s time
- Someone on your team spends hours every week manually exporting, copying, cleaning and re-uploading data between systems, and that manual step is both a bottleneck and a source of errors
- A data feed you depend on breaks or arrives late without warning, and you usually find out from a broken report or a confused colleague rather than from an alert
- You have the same information living in several systems (a CRM, a billing tool, a product database, a spreadsheet), and consolidating them into one trustworthy view is a recurring, painful chore
- You are standing up a warehouse or analytics platform and need the reliable, scheduled, monitored pipelines that actually keep it fed, not a proof-of-concept load that ran once by hand
Our working method
The organising principle is that the happy path is the easy part and almost never where the problem is. Any competent engineer can move data when the source is up, the schema is stable and every row is clean. The work (and the reason pipelines are where data projects break), is everything else: the malformed record, the column that appeared upstream overnight, the source that was late, the run that failed halfway and needs re-running safely. So our method is to design for those cases first. Idempotency means a retry is always safe. Data quality checks at the boundaries mean bad data is caught rather than propagated. Incremental loading and change data capture keep the pipeline affordable as it grows. And monitoring is non-negotiable, because a pipeline you cannot observe is one that will corrupt your data silently and be trusted while it does.
The second half of the method is honesty about the choices that have lasting consequences. We will tell you when scheduled batch is the right answer and streaming is complexity you do not need; when ELT with dbt is the sensible default and when classic ETL genuinely fits your constraints better; and which orchestration tool suits your team rather than which is fashionable this year. And we build to be handed over, clear lineage, supported backfills, a runbook that lets an on-call engineer who did not write the pipeline recover it, because a pipeline only one person can operate is a liability dressed up as an asset, and we would rather leave you something your own team owns.
Technologies we build it with
Chosen per problem, not per fashion. This is the stack we most often reach for on this work.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves, including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Want a straight answer on ETL Development?
A short call with a senior engineer, before you write a brief. If ETL Development is the wrong answer for your situation, we will say so and tell you what we think is right.
What changes
The manual wrangling gone
The weekly export-clean-reupload ritual replaced by a scheduled pipeline that does it reliably, correctly and without a person in the loop, freeing the time and removing the copy-paste errors that came with it.
Feeds you can trust
Data that arrives on schedule, is checked for quality at the boundary, and raises an alert to a human the moment a load fails, runs late or looks wrong, so problems are caught in minutes, not discovered in a report days later.
One consolidated source
Several scattered source systems reconciled into a single, consistent, dependable dataset, so people stop arguing about which system is right and start deciding on one set of numbers.
Industries we serve
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
How pricing works
- Fixed-scope build for a well-defined pipeline or set of pipelines (a known list of sources, a defined target and an agreed schedule), quoted once the sources, their behaviour and the transformation rules are understood, so you are not paying for scope nobody has pinned down.
- Focused audit or rescue for existing pipelines that are unreliable, slow or unmonitored. A review of where they break, what is missing in quality checks and observability, and a prioritised fix plan, priced by the scope of the problem, with the findings and the fix order as the deliverable.
- Monthly senior engagement for an evolving data platform, where sources come and go, schemas change and the pipeline estate grows with the business, and you want continuity on reliability, quality and operational health rather than a one-off deliverable handed over and forgotten.
Typical timeline
- 01
Discovery and source mapping
One to two weeks understanding the source systems and how each really behaves, the freshness the business needs, and the failure modes, then deciding batch versus streaming, ETL versus ELT, and the orchestration approach, and mapping the current state including the manual steps and the feeds that already break.
- 02
First pipelines and orchestration
Building the initial pipelines end to end (extraction, transformation, load), under orchestration, with idempotency, incremental loading and retries in place from the start, proven against real data one source at a time rather than assembled all at once.
- 03
Data quality and monitoring
Adding the quality checks at the stage boundaries (freshness, volume, null, uniqueness, referential and business-rule tests), and the monitoring and alerting on run status, lateness, volume anomalies and schema drift, so failures surface to a human immediately rather than in a report days later.
- 04
Hardening and handover
Testing recovery and backfills against realistic volumes, handling late-arriving data, documenting the lineage, schedule and runbook, and handing over so your own team can operate, recover and extend the pipelines without depending on the people who built them.
What working with us actually means
Senior engineers only
Pipelines are easy to build for the demo and hard to build for the years afterwards. The idempotency, the schema-change handling, the late data, the backfill that has to be safe, the monitoring that catches silent corruption. The people building yours have run pipelines in production and been paged when one broke at 2am, so the unglamorous disciplines are there from the start. No juniors learning what a non-idempotent retry does to your data on your production feed.
We operate what we build
Because we run the pipelines we design, the operational reality drives the design: monitoring, alerting, recovery procedures and backfills go in from the first pipeline rather than after the first silent failure. We optimise for a pipeline that keeps working and keeps telling you the truth as data grows and sources change, not for a load that ran clean once against a sample and fell over the first time reality diverged from it.
Reliability and observability as the actual job
We treat the parts most people skip (the failure handling, the quality checks, the monitoring), as the point of the work rather than an afterthought, because that is where pipelines actually break and where trust in the data is won or lost. A pipeline with no observability is not finished in our view; it is just untested in production, and we will not hand you one and call it done.
Honest about the trade-offs
We make the decisions with lasting consequences on your situation, not on what is fashionable, batch versus streaming on the freshness you genuinely need, ELT versus ETL on your warehouse and constraints, the orchestration tool on your team. We will happily talk you out of the real-time streaming pipeline you do not need, because the simpler, cheaper batch job you will actually be able to operate is usually the better answer.
How to engage us
Three ways to work with us on this, chosen to fit the problem, not our margin.
- Dedicated team A standing team that works only on your product, in your rituals and your tooling. Best when the roadmap outlives the project. Ongoing product development
- Staff augmentation Named senior engineers embedded into your existing team, reporting into your leads. Best when you know what to build and need capacity. Filling a capability gap
- Software outsourcing A defined outcome delivered end-to-end by an accountable team. Best when you want the result owned, not just the hours filled. Outcome-owned delivery
Related services
Part of Data Engineering. Other work we do alongside this.
Common questions
What is the difference between ETL and ELT, and which do we need?
Both move data from your sources into a warehouse; they differ in where the transformation happens. Classic ETL transforms the data on the way in and loads the finished result. The modern ELT pattern loads the raw source data into the warehouse first and transforms it there, typically with a tool like dbt, keeping the untouched raw data as something you can always recompute from. For most warehouse-centric work ELT has become the sensible default, because it separates ingestion from logic, keeps an immutable raw copy, and makes your transformations version-controlled, tested SQL, so when the logic changes or a bug turns up, you fix the model and rebuild from the raw data rather than re-extracting from the source. Classic ETL still fits where the warehouse should not hold raw data for privacy or volume reasons, or where the transformation is heavy and better done in a dedicated processing layer. We make the call on your warehouse, your volumes and how often your logic changes.
Do we need real-time streaming, or is a scheduled batch pipeline enough?
Usually a scheduled batch pipeline is enough, and it is simpler, cheaper and far easier to operate and reason about. The right question is not whether real-time sounds better (it always does), but how fresh the data genuinely needs to be for the decisions being made on it. If people act on the data hourly or daily, a batch pipeline on that cadence is the right tool. Streaming, with change data capture off your source database and a continuously running processing layer, earns its place when the business genuinely needs data within seconds rather than hours: fraud checks, live operational dashboards, that sort of thing. It is a meaningfully more complex thing to build and to keep running, so we reach for it when the requirement justifies the cost, and we will tell you plainly when it does not.
What actually happens when a source changes its schema or sends bad data?
This is the question that separates a pipeline that lasts from a script that limps, because both of these will happen. A well-built pipeline expects them. Data quality checks at the stage boundaries (type, null, uniqueness, referential and business-rule tests), catch bad or malformed records and fail the run loudly rather than importing the corruption and letting it propagate into your reports. Schema drift is detected and alerted on, so a column that appeared or changed shape upstream overnight raises a flag to a human instead of silently breaking a transform three steps downstream. The point is that these are treated as expected events with defined handling and alerting, not as edge cases someone hopes will not occur, because the pipeline that assumes clean, stable input is exactly the one that corrupts your data in silence.
Why does monitoring matter so much for a pipeline?
Because a pipeline that fails visibly is a minor problem, and a pipeline that fails silently is a serious one. When a load stops running entirely, someone usually notices the missing data. The dangerous failure is subtler: a partial load, a source that went stale, a transform that started producing slightly wrong numbers. The pipeline keeps running, the dashboards keep updating, and everyone keeps making decisions on data that quietly drifted out of true, sometimes for weeks, until someone happens to spot that a figure looks off. Monitoring on freshness, row volumes, run status and schema drift is what turns those silent failures into an immediate alert to a human. In our view a pipeline without monitoring is not finished; it is just untested in production, and it will eventually corrupt your data while being trusted the whole time it does.
How is this different from data warehouse development or big data work?
They are related but distinct. Data warehouse development is about the destination: the place the data lands and how it is modelled and organised for analysis. Big data work is about the techniques you reach for once the sheer volume or velocity of data breaks the ordinary tools. ETL development is the connective tissue between your source systems and that destination: the pipelines that reliably extract, transform and load the data on a schedule, with the quality checks and monitoring that keep it trustworthy. In practice these overlap on a real project and we often do them together (a warehouse is not much use without the pipelines that feed it), but if what you have is data that needs to move reliably from where it is created to where it can be used, the movement itself is this service.
Thinking about ETL Development?
Tell us the problem in your own words, not in requirements. A senior engineer reads it and comes back with a straight view on whether ETL Development is the right answer here, or what would be.
- 01A senior engineer reads it. Not a form queue, and not an account manager.
- 02We reply either with questions or with a straight answer that we are not the right fit.
- 03If it looks like a fit, a technical call with the person who would actually run the delivery.
- 04Then scope, effort and risk in writing, before anyone signs anything.