Data Engineering

Data Engineering Services for Enterprise Data Platforms

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.

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Key takeaways

DS Stream delivers data engineering for enterprise data platforms: lakehouse architecture, real-time pipelines, governance and lineage, data quality and observability, and self-service data products.
Most engagements start on a platform you already pay for: Databricks, Snowflake, BigQuery or open-source Spark, with Airflow or Dagster for orchestration, dbt for transformation logic, and Kafka or Kinesis when events have to land in seconds.
Reliability and cost are engineered alongside the pipelines: automated tests, freshness and volume checks, column-level lineage, right-sized clusters, and platform spend on a dashboard finance can read.
Delivery runs in four phases: data strategy in weeks 1-3, platform build in weeks 4-12, scaling data products in weeks 13-24, then ongoing operations and governance.
Where a migration makes sense, workloads move in waves and the old path keeps running until the new one is proven.

What do data engineering services include?

They cover ingestion, transformation, storage, modeling, orchestration and access control for an enterprise data platform. DS Stream builds batch and streaming ingestion, transformation pipelines, lakehouse storage layers, dimensional and data vault models, orchestration, governance and quality checks, then operates the result as a running product rather than a one-off build.

Lakehouse architecture - raw, cleansed and curated layers on Databricks, Snowflake, BigQuery or open table formats such as Delta and Iceberg.
Real-time data pipelines - event streams, devices and change data capture processed with Kafka, Kinesis, Spark Structured Streaming or Flink.
Data governance and lineage - access control, cataloging and column-level lineage inside the platform itself.
Data quality and observability - automated expectations for schema, volume, freshness and business rules.
Self-service data products - curated datasets with documentation, an owner, a freshness service level and a stable contract.

Why do 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 correct, or who to call when the morning load fails. Ad-hoc pipelines pile up faster than anyone documents them, and analysts rebuild the same logic in three tools.

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.

How are reliability and platform cost controlled?

Both are 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 make platform spend visible to finance.

Automated expectations for schema, volume, freshness and business rules run inside the pipeline, so bad records are quarantined before they reach a dashboard.
Tooling includes 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.
Platform spend sits on a dashboard finance can read, with alerts when a workload drifts outside its budget.

Who is this data engineering service for?

It is built for the four roles that carry an enterprise data platform: the Chief Data Officer accountable for value and governance, the Head of Data Engineering whose team is buried in incidents, the Head of Analytics or BI waiting in an engineering queue, and the Head of AI or ML whose models fail for data reasons.

Chief Data Officer - a governance model that survives an audit, a portfolio view of data products and their owners, and a cost line you can defend in a budget review.
Head of Data Engineering - reusable ingestion, transformation and deployment patterns, test coverage on critical paths, documented code and runbooks at handover.
Head of Analytics / BI - governed data products with documented definitions, known freshness and a named owner, plus shared semantic models so two reports on the same metric agree.
Head of AI / ML - training and inference data with versioning, lineage and quality guarantees, feature pipelines that behave the same in batch and streaming, and the retrieval layers AI applications need.

What we deliver

Data engineering capabilities

Each capability is delivered on the platform you already run and handed over with documented code and runbooks.

Lakehouse architecture

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
Raw, cleansed and curated layers defined explicitly
Partitioning and file sizing set for the queries you actually run
Catalog wired in, so every table has an owner, a schema contract and a retention rule

Real-time data pipelines

Event streams from applications, devices and change data capture
Processing with Kafka, Kinesis, Spark Structured Streaming or Flink
Data landed where analysts and services can use it within seconds
Ordering, late and duplicate events, and replay after an outage handled explicitly
Schema registry that stops a producer change from silently breaking downstream consumers

Data governance and lineage

Access control, cataloging and column-level lineage implemented inside the platform itself
Unity Catalog, Snowflake Horizon, Collibra or open-source options such as OpenMetadata
Connected to your identity provider
Analysts see where a number came from; auditors see who touched it
Sensitive fields masked by policy instead of by convention

Data quality and observability

Automated expectations for schema, volume, freshness and business rules
Checks run inside the pipeline, so bad records are quarantined before a dashboard
Great Expectations, dbt tests or native platform checks
Monitoring of job duration, cost per run and failure patterns
Alerts routed to the dataset owner with enough context to act

Self-service data products

Curated datasets published with documentation, an owner and a freshness service level
Stable contracts so analytics, finance and AI teams can serve themselves
New consumers subscribe from the catalog
Breaking changes go through versioning and deprecation
Platform team keeps the paved road; domain teams keep the business logic

How we build your data engineering practice

Four phases, from an architecture baseline you can use with or without us to an operating model your own team can carry.

Phase 1 - Data Strategy (weeks 1-3)
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.
Phase 2 - Platform Build (weeks 4-12)
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.
Phase 3 - Scale Data Products (weeks 13-24)
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.
Phase 4 - Operate and Govern (ongoing)
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.
Source: DS Stream, Data Engineering services page
99.9%
Data SLA on critical data products with automated quality and freshness monitoring.
Source: DS Stream, Data Engineering services page
3x
Faster analytics velocity through self-service consumption of governed data products.
Source: DS Stream, Data Engineering services page

From ad-hoc pipelines to a governed platform

Four failure patterns we see in enterprise data estates, and the structural fix for each.
Failure pattern	Typical current state	After the fix
Pipeline sprawl	Scripts, notebooks and scheduled jobs pile up across teams with no inventory and no lineage, so every change is a gamble	Dead paths retired and the rest moved onto shared, tested ingestion and transformation patterns one team can support
Data quality crisis	Errors are found by a controller reconciling a report, or by a model training on duplicated rows	Validation sits in the pipeline: schema drift, volume drops and rule violations stop the load and page an owner
Ownership gaps	A dataset belongs to a project, so maintenance ends the day that project closes	A named owner per critical data product, with a written contract on freshness, schema stability and support, recorded in the catalog
Slow self-service	Every new question needs a ticket and a sprint, so analysts stop asking	Governed, documented datasets, shared semantic models and access rules that let people query safely on their own

Most common questions about data engineering

What is a data product?

A data product is a curated dataset published with documentation, a named 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 instead of arriving unannounced.

Which platforms and tools do you work on?

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.

Do we have to migrate off our current platform first?

No. We build on the platform you already run. Where a migration does make sense, we move workloads in waves and keep the old path running until the new one is proven, so reporting the business cannot lose stays available throughout the change.

How long before the platform serves real users?

Phase 1, the data strategy, runs in weeks 1-3. Phase 2, the platform build, runs in weeks 4-12 and ends with data products serving real users in production. Scaling new domains in waves then runs through weeks 13-24, reusing the patterns proven in the build phase.

How do you keep platform spend under control?

Cost is engineered with the pipelines. 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. Phase 1 also produces a cost baseline for the platform you run today.

Can you take over an existing data estate instead of building a new one?

Yes. 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. We refactor around live workloads rather than freezing them, so delivery continues during the work.

Who owns the platform after the engagement?

Your team does. A platform team maintains the paved road, tooling and standards while domain teams own their data products. We run or co-run operations as long as needed, including on-call for critical loads, incident reviews and cost tuning, then train your engineers, hand over runbooks and step back.