Data Engineering Services for Enterprise Data Platforms
Data engineering services for enterprise lakehouse platforms: ingestion, batch and streaming pipelines, governance, quality and cost control.
Lakehouse, real-time pipelines, and governed data products — built for scale
Our data engineering services cover the whole path from source system to consumption: batch and streaming ingestion, transformation pipelines, lakehouse storage layers, dimensional and data vault modeling, orchestration, and the access rules that decide who sees what. We take on the platform as a running product, not as a one-off build.
Most engagements start on a platform you already pay for. We work on Databricks, Snowflake, BigQuery and open-source Spark, with Airflow or Dagster for orchestration, dbt for transformation logic, and Kafka or Kinesis where events have to land in seconds rather than hours. Where a migration makes sense, we move workloads in waves and keep the old path running until the new one is proven.
Reliability and cost get engineered at the same time as the pipelines. Every critical dataset carries automated tests, freshness and volume checks, column-level lineage, and an owner who is alerted before the business notices. On the cost side we right-size clusters, tune file layout and partitioning, and put platform spend on a dashboard finance can read, with alerts when a workload drifts outside its budget.
Why Data Initiatives Fail to Scale
Data programs rarely stall on technology. They stall when nobody can say which pipeline produced a number, which copy of the customer table is the correct one, or who to call when the morning load fails. Ad-hoc pipelines pile up faster than anyone documents them, quality checks get added after the first incident, and analysts rebuild the same logic in three tools because none of them trusts the others.
Our data engineering services start by making the current state visible: an inventory of sources, pipelines and consumers, lineage for the reports the business actually uses, and an honest list of what breaks. The fix after that is structural. Shared ingestion and transformation patterns replace one-off scripts, every dataset gets a named owning team, and quality rules move into the pipeline, where they block bad records at load time. Delivery continues throughout, because we refactor around live workloads rather than freezing them.
Data Engineering Capabilities
One storage and compute layer for analytics, reporting and machine learning, built on Databricks, Snowflake, BigQuery or open table formats such as Delta and Iceberg. We define the raw, cleansed and curated layers, set partitioning and file sizing for the queries you actually run, and wire in the catalog so every table has an owner, a schema contract and a retention rule.
Event streams from applications, devices and change data capture, processed with Kafka, Kinesis, Spark Structured Streaming or Flink, then landed where analysts and services can use them within seconds. We handle ordering, late and duplicate events, replay after an outage, and the schema registry that stops a producer change from silently breaking every downstream consumer.
Access control, cataloging and column-level lineage implemented inside the platform itself. We work with Unity Catalog, Snowflake Horizon, Collibra or open-source options such as OpenMetadata, connected to your identity provider. Analysts can see where a number came from, auditors can see who touched it, and sensitive fields are masked by policy instead of by convention.
Automated expectations for schema, volume, freshness and business rules, running inside the pipeline so bad records are quarantined before they reach a dashboard. We use Great Expectations, dbt tests or native platform checks, plus monitoring of job duration, cost per run and failure patterns. Alerts go to the dataset owner with enough context to act, not to a shared inbox.
Curated datasets published with documentation, an owner, a freshness service level and a stable contract, so analytics, finance and AI teams can serve themselves. New consumers subscribe from the catalog, and breaking changes go through versioning and deprecation. The platform team keeps the paved road; domain teams keep the business logic they understand best.
How We Build Your Data Engineering Practice
We map sources, pipelines, consumers and the reporting the business cannot lose, then measure the distance to a target architecture. The output is concrete: architecture decision records, a governance and ownership model, a prioritized backlog of data products, and a cost baseline for the platform you run today. It is usable whether you build with us or without us.
Infrastructure as code, ingestion for the first source systems, the transformation framework, orchestration, catalog and quality tooling, all deployed through CI/CD into your own cloud account. The phase ends with data products serving real users in production, so the platform proves itself on workloads your teams already depend on.
New domains onboard in waves, reusing the patterns proven in the build phase. Each wave adds data products with tests, lineage, documentation and agreed service levels, and each wave should cost less effort than the one before. We track that curve, because a platform that does not lower the marginal cost of delivery is not finished.
A platform team maintains the paved road, tooling and standards while domain teams own their data products. We run or co-run operations for as long as you need: on-call for critical loads, incident reviews, cost tuning, version upgrades and security patching. In parallel we train your engineers and hand over runbooks, then step back once your team can carry the platform alone.
Business Impact
60% reduction in time to deliver new data products through reusable platform components.
99.9% data SLA on critical data products with automated quality and freshness monitoring.
3x faster analytics velocity through self-service consumption of governed data products.
Single source of truth with documented lineage and ownership across the enterprise.
Who This Is For
Use Cases for Data Engineering
We deliver data engineering services for regulated, data-heavy industries: banking and insurance, telecommunications, retail and consumer goods, manufacturing, logistics, and life sciences. Vertical knowledge matters most at the modeling layer, where a policy, a shipment or a batch record follows rules that a generic schema will not survive.
Typical engagements: consolidating scattered warehouses and lakes onto one lakehouse after a merger or a cloud migration; building near real-time customer and event pipelines behind personalization and fraud checks; industrializing IoT and telemetry ingestion from plants and vehicles; standing up regulatory and management reporting with lineage that holds up in an audit; and preparing training and retrieval data for AI teams that have run out of patience with manual extracts. Some clients want one platform built end to end. Others need a data platform engineering team to take over an existing estate, stabilize it, and hand back documented, tested pipelines.
Most Common Questions About Data Engineering
A data product is a dataset treated like a product: it has a named owner, documented meaning for every field, automated quality tests, a service level for freshness, and a schema contract that does not change without notice. Consumers find it in the catalog and use it directly, without asking an engineer for an extract. What separates it from an ordinary table is accountability. Someone is responsible for it, someone is alerted when it breaks, and its consumers are known by name.
A warehouse gives you fast SQL, strong governance and structured data at a predictable cost. A lake stores anything cheaply but leaves consistency and performance to you. A lakehouse puts open table formats such as Delta or Iceberg over cheap object storage and adds transactions, schema enforcement and time travel, so one copy of the data serves BI, engineering and machine learning. Most enterprises we work with converge on a lakehouse, though a warehouse stays the right answer when workloads are purely structured and driven by SQL.
The one that fits your workloads, your team's skills and the commercial agreements you already have. We build on Databricks, Snowflake, BigQuery and open-source stacks around Spark, Airflow and dbt, and we work across all three major clouds, so the recommendation is not shaped by what we happen to know. In practice the decision comes down to streaming requirements, machine learning workloads, governance obligations and total cost at your data volumes. We document the trade-offs and the assumptions behind them, so the choice can be revisited later.
Governance is built into the platform from the first sprint, not bolted on as a review stage later. Access control runs through your identity provider with roles mapped to data domains, sensitive columns are masked or tokenized by policy, and lineage is captured automatically from the pipelines. We implement this with Unity Catalog, Snowflake Horizon, Collibra or open-source tools such as OpenMetadata, whichever fits your stack. The practical test is simple: an auditor should be able to trace any number in a report back to its source without an engineer in the room.
Quality rules live in the pipeline, so bad data is caught on the way in rather than discovered in a dashboard. We define expectations per dataset for schema, volume, freshness, uniqueness and business logic, then run them with Great Expectations, dbt tests or native platform checks at every stage. Failures quarantine the affected records and alert the dataset owner with the context needed to fix them. Observability sits on top, tracking job duration, cost and failure patterns over time, which is how our data engineering services catch a degrading source before anyone downstream notices.
Ready to Industrialize Your Data Engineering Practice?
The first conversation is a working session, not a pitch. Bring your current architecture, your worst recurring incident, and the reports the business cannot afford to lose. We will tell you what we would fix first, what we would leave alone, and what each option costs in engineering effort.
Delivery runs from Poland, with engineers in European time zones, daily overlap with US mornings, and data kept inside the EU under GDPR. Teams are senior, English-speaking and embedded in your own process rather than sitting behind a ticket queue. Engagements range from a short data engineering consulting review to a dedicated platform team on fixed scope or time and materials, and every deliverable ships with documentation, tests and runbooks so your people can operate it without us.
Data Strategy Workshop
Two weeks with your data, architecture and business stakeholders. We audit the current estate, agree the target architecture and governance model, and rank candidate data products by business value against engineering effort. You leave with a roadmap, a cost baseline and a scoped proposal for the first build.
Platform Foundation
We deploy the lakehouse foundation in your own cloud account: infrastructure as code, ingestion for the first sources, the transformation framework, orchestration, catalog, access control and quality tooling. Everything ships through CI/CD with environments separated, so the platform is ready to carry production workloads.
First Data Products
The first governed data products go live for analytics and AI teams, each with tests, lineage, documentation, a named owner and an agreed freshness target. Alongside them you get the runbooks and onboarding material your engineers need, so the next data products can be built by your own team using the same patterns.