Data your teams can trust, from the source system to the dashboard.

Most analytics problems are data problems in disguise. We stabilize how data arrives, govern what it means, and build the platform that analytics and AI can safely stand on.

Talk to usSee what it solves

What data engineering solves

The questions that bring people to us for this work, what is usually behind them, and what we do about it.

  • “Why does every report need a week of cleanup?”

    The problem

    Ingestion is improvised: each feed has its own format, and quality problems surface in the dashboard instead of the pipeline.

    How we solve it

    A source registry and a data contract for every feed, with quality checks, lineage, and freshness built into the pipelines.

  • “Which number is the right one?”

    The problem

    Each team defines metrics its own way, so leaders argue about data instead of decisions.

    How we solve it

    Governed schemas and one shared metric layer, so a KPI means the same thing in every report.

  • “Can this platform carry AI, or will it buckle?”

    The problem

    Models are trained on one-off extracts that nobody can reproduce or refresh.

    How we solve it

    Reusable transformations and feature stores that feed analytics and machine learning from the same governed source.

Each answer rests on the same method. Here it is for data engineering.

How we do it

Four steps, in order, with governance designed in from the first one.

  1. Register the sources

    Catalog every operational system, SCADA or IoT feed, permitting system, and field tool, each with an owner and a contract.

  2. Build the pipelines

    ETL and ELT with quality checks, lineage, and freshness monitoring on every run.

  3. Shape the platform

    Warehouse or lakehouse architecture in your cloud, with governed schemas and role-based access.

  4. Serve decisions

    Dashboards and data products organized around the decisions they inform, not the tables they come from.

Data governance in this work

A pipeline without governance just moves problems faster.

  • Every dataset has an owner, a contract, and a documented schema.
  • Lineage from every figure back to its source, captured automatically.
  • Role-based access and audit logs on sensitive data from the first release.

What you get

  • A source registry and data contracts
  • Production pipelines with quality monitoring
  • A governed data platform in your cloud
  • The first decision dashboards
  • Runbooks and training for your team

A method is only useful once it is running. Here is how it gets there.

Phase by phase, with a gate at each one

Each gate is agreed before its phase begins, so nothing moves forward on optimism.

  1. Foundation

    Platform set up, data cataloged, and the first decision dashboards live.

    Gate: First dashboard in use

  2. Production

    The first models in production with monitoring, and authoritative data published.

    Gate: Monitoring live

  3. Expansion

    Data products shared across departments under formal sharing agreements.

    Gate: Sharing agreements signed

  4. Review

    Model risk assessment, portfolio review, and cost tuning.

    Gate: Ongoing

How it runs

Built in your cloud (AWS, Azure, or Google Cloud, including their government regions), on tools your team can run after handover.

Technical detail

Data engineering

  • Source registry and data contracts across operational, SCADA and IoT, permitting, and field systems
  • ETL and ELT pipeline development
  • Cloud data platform migration, including AWS GovCloud and Azure Government
  • Data lake and warehouse architecture
  • Real-time streaming for emergency response, traffic, and environmental monitoring
  • Metadata, lineage, and quality checks embedded in pipelines
  • Reusable transformations and feature stores

Use cases by sector

  • Utilities: network planning, vegetation management, outage probability mapping, leak detection triage
  • Builders and developers: site selection inputs combining zoning, utilities, flood and fire risk, traffic, and permit timelines
  • Cities and school districts: inspection prioritization, right-of-way analytics, service equity dashboards, safe routes, facilities planning

Tell us which report takes longest to produce.

We will trace it back to its sources and show you what it takes to automate it.

Talk to us

What we build