Data Engineering

Reporting your team can trust, because the pipeline under it can be checked.

When two dashboards show two different revenue numbers, the chart is rarely the problem. We build the ingestion, models and quality checks behind your reporting, so a number can be traced back to its source and rerun when the source changes.

Scope your data engineering work →

DIRECT ANSWER

Data engineering turns raw exports, databases and APIs into modeled, validated tables and the dashboards built on them. This engagement covers agreed sources, a warehouse model, quality checks and reports, with reruns and ownership documented.

Best for

Teams whose reporting lives in spreadsheets, manual exports or disagreeing dashboards
A product or operations team that needs a defined set of metrics it can defend

Consider another approach

Moving a live record from one business system into another
A chat assistant that answers questions from internal documents

Start with the decision, not with the tool

Pick the report that drives a real decision and work backwards to the sources it needs. That keeps the first scope bounded and testable. Moving an order or a customer record between operational systems is API Integration; this page covers the analytical copy built from those systems. A chat interface over internal documents belongs with the Internal AI Assistant.

What the pipeline needs beyond a scheduled job

A dashboard is only as reliable as the path that feeds it. We define each layer against your actual sources and volume, and pick the warehouse (often plain Postgres, sometimes a managed cloud warehouse) by what your team can operate.

Ingestion that survives reruns

Load APIs, databases and file exports idempotently, so running a job twice does not double a number. Keep the raw copy so a model can be rebuilt after a fix.

Models with written definitions

Agree what an active customer, a net order or a churned account means, then encode it once. Every report reads the same definition.

Quality checks that stop bad data

Validate row counts, required fields, units and schema changes at load time. A vendor that silently renames a column should fail a check, not shift a chart.

Dashboards people actually open

Build the agreed reports in a BI tool your team already licenses or one we recommend, with filters and drill-downs that match how the business asks questions.

Illustrative acceptance: a drifting export gets caught

Synthetic example, not a client result: a supplier export arrives with a column renamed and a date format changed. The load check rejects the file, the previous model stays in place and the owner gets an actionable message. After the mapping is corrected, the same file is rerun and the totals match the source. The record names the file, the check, the expected and observed result and who accepted the fix.

Assess, build and hand over in phases

Each phase is scoped and quoted before work starts. You can take the assessment to your own team or commission the build.

Assessment
Receive a source inventory, the metric definitions in dispute, data quality findings and a prioritized model and report backlog.
Build and acceptance
Receive the agreed pipelines, models, checks and dashboards in your environment, tested against representative data and accepted by a named owner.
Continued support
New sources, metrics and reports are agreed separately with a scope and response responsibilities. This does not establish 24/7 coverage or an uptime SLA.

Bring access, examples and the person who owns the number

We need read access to the agreed sources (or representative exports), the reports you use today, the questions they fail to answer, the expected volume and refresh cadence, and a named owner for each metric definition. Personal data is minimized or pseudonymized in the analytical copy where the reports allow it. Who may see which report is agreed before the first dashboard goes live.

Your warehouse, your definitions, your code

Pipelines, models and dashboard definitions live in your repository and your accounts. The handover covers how to add a source, how to rerun a load and what to do when a check fails. If the data comes from field devices or machines, the telemetry side is scoped in Industrial IoT and handed into the same warehouse.

// proof

Data work in existing engineering engagements

Bond Analytics documents a validated Excel ingestion path with idempotent reruns, a snapshot-shaped Postgres schema and a React dashboard. MyMerch documents custom operational dashboards across its Shopify stores. Together they show the data and reporting work we have published, not a promise of the same result for your sources.

2 months 0 to Enterprise

Institutional bond analytics platform, zero to enterprise-ready in 2 months; raised multiple rounds.

NestJS React Typescript

~50% Efficiency improvement for the MyMerch team

MyMerch’s team puts the efficiency gain at ~50%. Fewer delivery errors, improved Shopify conversion, no rewrite.

Shopify Shopify Liquid JavaScript

These are selected projects, not our full portfolio. We have shipped 100+ products since 2018.

Questions before you start

Do we need a cloud data warehouse?

Not by default. Many reporting needs fit a well-modeled Postgres database. A managed warehouse earns its cost with larger volumes, many concurrent users or heavy ad-hoc queries. We recommend one after seeing the sources and the query patterns.

Which BI tool do you use?

Preferably the one your team already licenses and knows. If there is none, we compare a few options on cost, access control and who will maintain the reports, then recommend one.

How is this different from API integration?

API integration moves an operational record so another system can act on it. Data engineering copies and models data so people can analyze it. Both can share a source; the owner, freshness and failure handling differ.

Can you work with spreadsheets and manual exports?

Yes. Many real pipelines start there. We validate every file at load time and make reruns safe, so a late or corrected export does not corrupt history.

Who maintains the pipelines afterwards?

Agree that before handover. Your team can run them with the documentation we deliver, or ongoing support is scoped separately. It is not an implied subscription.

Scope your data engineering work

Name the sources, the report you need and the number nobody currently trusts. Describe the data, do not send it. Exports with customer records do not belong in the first message.