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Data Engineering & MLOps

Data engineering & MLOps services

We connect your sources, model your data in a warehouse or lakehouse, and set up the pipelines and MLOps tooling that keep analytics and AI running without manual fixes.

A custom engineering engagement

We scope this service around your workflow, existing systems and acceptance criteria. The work starts with the problem and its constraints, then a written scope and price. A packaged solution is a different buying route: choose it when its defined deliverables already fit the job.

01The problem

Where things usually stall

Pipelines, warehouses and ML infrastructure that keep data and models fresh.

  • 01

    Reports that disagree

    Finance and sales pull the same metric from different systems and get different numbers.

  • 02

    Pipelines held together by cron

    One schema change upstream and the Monday dashboard is empty.

  • 03

    Models nobody can retrain

    The person who trained the model left, and the steps to rebuild it lived in their head.

02Capabilities

What we deliver

  • 01

    Source integration

    Connectors for SaaS tools, databases, files and event streams, with change data capture where it matters.

  • 02

    Warehouse and lakehouse design

    Data models in Snowflake, BigQuery, Postgres or Databricks that match how the business asks questions.

  • 03

    Pipeline orchestration

    Scheduled and event-driven pipelines with tests, retries and lineage.

  • 04

    Data quality monitoring

    Freshness, volume and schema checks that alert the owner before a dashboard breaks.

  • 05

    ML pipelines and registries

    Reproducible training, versioned models and one-click redeploys.

  • 06

    BI enablement

    Semantic layers and dashboards your teams use without filing a ticket.

03Outcomes

What changes once it’s live

  • 01

    Traceable data movement

    Pipelines with clear source ownership, transformations and destinations.

  • 02

    Failures that surface

    Freshness checks, validation and alerts distinguish late data from genuinely empty data.

  • 03

    Reproducible model operations

    Versioned datasets, deployment configuration and release records support controlled changes.

04Approach

How we deliver it

  1. 01

    Map sources and consumers

    Identify who produces each dataset, who uses it and which downstream decisions depend on its freshness.

  2. 02

    Agree contracts and access

    Define schemas, quality expectations, retention and permissions before building the pipeline.

  3. 03

    Build for replay and recovery

    Implement incremental processing, backfills, orchestration and validation that can handle delayed or duplicated events.

  4. 04

    Make operations transferable

    Document lineage, alerts, deployment steps and ownership so the pipeline can be maintained by your own team.

05Stack

Tools we use most

  • Snowflake
  • PostgreSQL
  • Python
  • Docker
  • Kubernetes
  • ClickHouse
  • Kafka
  • DynamoDB
  • BigQuery
  • Cassandra
  • TimescaleDB
  • MLflow

07FAQ

Frequently asked questions

View pricing guide
Do we need a data warehouse?

If more than two systems feed your reporting or AI, almost always. We size it to your volume, and many clients start on managed Postgres or BigQuery.

Can you work with our existing BI tool?

Yes. We model the data once and connect it to whatever your teams already use.

Who owns the pipelines after launch?

You do. Everything runs in your cloud and lives in your repositories, documented, with runbooks for your team or ours.

How is the engagement priced?

We agree a fixed scope and price after reviewing the work, system dependencies and acceptance criteria. The pricing guide explains approved ranges for the engagement types it covers. A written proposal confirms what is included before a build starts.

How should the first delivery decision be made?

Identify who produces each dataset, who uses it and which downstream decisions depend on its freshness. Define schemas, quality expectations, retention and permissions before building the pipeline.

What should the acceptance criteria cover?

Pipelines with clear source ownership, transformations and destinations. Freshness checks, validation and alerts distinguish late data from genuinely empty data. Versioned datasets, deployment configuration and release records support controlled changes.

AI readiness

Not sure where you stand?

Ten questions, about three minutes. Find out what needs attention before you build.

Data Engineering & MLOps

Data Engineering & MLOps

Pipelines, warehouses and ML infrastructure that keep data and models fresh.