Skip to content

Data Engineering

Our data engineering services build the foundation your reporting, analytics, and AI depend on: ingestion pipelines from every source system, a cloud data warehouse or lakehouse, tested transformations, and streaming where the business needs data in real time. With quality checks, lineage, and clear ownership built in, your teams get numbers they can trust and stop arguing about whose spreadsheet is right.

Start a project

When two dashboards disagree, nobody trusts either of them

Most companies don't have a data shortage. They have the same customer in four systems under four different IDs, a revenue number that depends on who you ask, and a nightly job that fails quietly until someone notices the dashboard stopped moving. Every analyst and every AI project downstream inherits that mess.

Our data engineers build the layer that fixes it: pipelines that pull reliably from every source, a warehouse or lakehouse modeled around how the business actually works, and transformations tested like application code. Freshness and quality are monitored, ownership is written down, and the metrics everyone argues about get a single definition.

Tested

Every transformation, with data checks on every run

1 definition

Per business metric, versioned in code

Alerted

On late or broken data before anyone opens a dashboard

Yours

Pipelines, models, and docs in your own cloud accounts

What's included

Data pipelines, warehouses, and streaming systems that turn scattered data into one trustworthy source for reporting and AI.

Batch and streaming data pipeline development
Data warehouse and lakehouse architecture
ELT modeling and transformation with dbt
Data quality testing and pipeline observability
Data governance, lineage, and catalogs
Real-time and event-driven data systems

Four principles behind a data platform people trust

Data platforms rarely fail because of the wrong tool. They fail because nobody can tell when the numbers are wrong, or who is responsible for fixing them.

01

Treat data as code

Transformations live in version control with reviews, tests, and CI, using dbt or Spark. Changing how revenue is calculated is a pull request, not a quiet edit in a BI tool.

02

Check quality in the pipeline

Schema, freshness, volume, and business-rule checks run inside the pipeline, so bad data is stopped and flagged before it reaches a report or a model.

03

Model for the business

Tables are shaped around customers, orders, and accounts, not around the quirks of each source system, so analysts can answer questions without a data engineer on call.

04

Make ownership visible

Every dataset has an owner, documentation, and lineage back to its source. When a number looks wrong, anyone can trace where it came from in minutes.

Data engineering services, from source systems to trusted metrics

Six kinds of data work we deliver, whether you are building a first data warehouse or repairing a platform nobody trusts anymore.

Data pipelines & ingestion

Reliable pipelines from your databases, SaaS tools, event streams, and files, with retries, backfills, and schema-change handling so a vendor update doesn't break reporting.

  • Managed and custom connectors
  • Incremental loads and backfills
  • Schema change detection and alerts

Data warehouse & lakehouse

A cloud data warehouse or lakehouse on Snowflake, BigQuery, or Databricks, sized to your real volumes and query patterns rather than the vendor's biggest tier.

  • Platform selection and architecture
  • Cost-aware storage and compute design
  • Access control and data security

Data modeling & transformation

dbt models that turn raw tables into clean, documented datasets and a shared metrics layer, so finance, product, and sales finally report the same numbers.

  • Dimensional and domain-based models
  • Shared metric definitions
  • Tested, documented dbt projects

Streaming & real-time data

Kafka and Flink pipelines for the cases that need data in seconds: fraud checks, live operations dashboards, inventory, and event-driven product features.

  • Event streaming with Kafka
  • Stream processing with Flink
  • Replay and exactly-once strategies

Data quality & observability

Automated tests, freshness checks, and anomaly alerts on your most important tables, routed to the people who own them, with a clear incident process.

  • Data tests and data contracts
  • Freshness and volume monitoring
  • Ownership and incident runbooks

Governance, lineage & catalogs

Documentation, lineage, and access policies that make data easy to find and compliant with GDPR or HIPAA, without slowing down the analysts who rely on it.

  • Data catalog and documentation
  • Column-level lineage
  • PII tagging and access policies

Our data engineering process, from audit to a platform your team runs

Most teams see their first trusted dashboard on the new platform within six weeks. We start from the questions the business most needs answered and build backward to the pipelines that feed them.

Stage 01

2 weeks

Data audit & architecture

We inventory source systems, existing pipelines, and the reports people rely on, find where the numbers diverge, and agree a target architecture and the first business area to tackle.

  • Source and pipeline inventory
  • Target architecture and tool choices
  • Roadmap ranked by business value
Stage 02

3–4 weeks

First domain, end to end

One business area, often revenue or customers, goes from source to tested model to dashboard, with orchestration, monitoring, and access control in place from the start.

  • Production pipelines for one domain
  • Tested models and metric definitions
  • Monitoring and alerting live
Stage 03

4–10 weeks

Expand & migrate

Remaining domains follow the same pattern. Legacy jobs and reports are migrated and reconciled against the old numbers before anything is switched off.

  • Additional domains in production
  • Reconciliation against legacy reports
  • Plan to retire the old pipelines
Stage 04

Ongoing

Handover & operations

Documentation, runbooks, and training for your data team, then optional support with agreed response times and regular cost and reliability reviews.

  • Platform documentation and runbooks
  • Training and pairing with your team
  • Support agreement and cost reviews

What a trustworthy data platform changes

One agreed number for revenue, customers, and every other core metric

Broken or late data caught by alerts, not by an executive in a meeting

Analysts answering their own questions instead of queuing for engineers

A clean, governed foundation ready for machine learning and AI

What we build it with

The tools we reach for on Data Engineering work, picked for the problem in front of us and for the team who inherits the code.

01

Ingestion

Getting data out of systems that resist it

  • Apache Kafka
  • Airbyte
  • Apache Flink
  • Apache Pulsar
02

Processing

Transformations that are tested like code

  • Python
  • Apache Spark
  • Polars
  • dbt
03

Storage

The warehouse and the lake, and when to use each

  • Snowflake
  • Databricks
  • BigQuery
  • ClickHouse
  • DuckDB
04

Orchestration

Scheduled, observed, and alerting on freshness

  • Apache Airflow
  • Prefect
  • Trino
  • Datadog

Data engineering questions we hear most

  • Data engineering builds and runs the infrastructure that moves, cleans, and stores data. Data science uses that data to answer questions, run experiments, and build forecasts. Engineering comes first: without reliable pipelines and agreed definitions, analysis rests on numbers nobody trusts. We offer both, and many clients start with data engineering, then bring in our data science team once the foundation is solid.

Explore more

Let's build something that lasts

Tell us about your project and we'll get back to you within 12 hours with next steps.

Start a projectSee our work