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Data Engineering & AnalysisOne number. One definition. One place to look.

Three departments bring three different revenue figures to the same meeting, and the meeting becomes about the numbers instead of the decision. The cause is almost never the dashboard tool. The definitions live in people’s heads and in the WHERE clauses of forty different queries. We fix the layer underneath.

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Commitments

Governed definition per metric
1
Freshness target for operational data
Minutes
Insight delivered where the work happens
In-flow

Data Engineering and Analysis

We start by reviewing all the data relevant to the project and agreeing on a plan. Then we build pipelines that pull that data in, clean it and organize it into models. We analyze those models and give you a report of what we found.

Aggregation
Bundling separate sources into one set you can analyze, often sales, marketing and performance figures that have never been read together
Visualization
Presenting that data to the people who act on it, your board, an investor or a client, as the charts and decks they already read
Business insights
Comparing strategies in numbers, typically a marketing campaign or a new sales training course, so you can tell which one worked

When would I want this?

  • You want to know what improvements your data says you should make
  • You want to see how well a strategy is working
  • You want to understand a critical part of your business, such as a new customer base
  • You want to design effective KPIs for your business
  • You want to see your progress in clear, meaningful numbers

Sounds like

You might recognize one of these.

  • Finance and ops report different numbers for the same month.

  • Our reports run overnight and are wrong by morning.

  • Every question takes an analyst three days.

  • We bought a BI tool and it changed nothing.

What you get

What lands on your side, and stays there.

  • Versioned pipelines with automated tests and lineage
  • Warehouse schema with a documented data dictionary
  • Semantic metric layer defined in code
  • Data quality monitors with named owners
  • Dashboards for the decisions that matter, and no others
  • Cost and freshness SLAs per pipeline

Shapes

How this usually runs.

  1. Data assessment

    2–3 weeks

    Source inventory, definition conflicts, quality baseline and a prioritized plan, including which reports to delete.

  2. Warehouse foundation

    8–14 weeks

    Ingestion, core models, the first set of governed metrics and the quality framework around them.

  3. Analytics enablement

    Ongoing

    New domains, self-service enablement and embedded analytics inside your own products.

What this includes

The work, specifically.

Not every engagement needs all of it. This is the range we cover and what each part is actually for.

  • Ingestion and change data capture

    Reliable, incremental extraction from operational databases, SaaS APIs, flat files and EDI feeds, with schema drift detection and replay after failure.

  • Warehouse and lakehouse modeling

    Dimensional models built for the questions the business actually asks, with tested transformations, documented lineage and enforced data contracts.

  • Semantic layer

    Metrics defined once, in version control, reviewed like code, and consumed identically by the BI tool, the API and the spreadsheet. This is what ends the three-numbers meeting.

  • Data quality and observability

    Freshness, volume, distribution and referential tests running on every load, with alerts routed to an owner rather than a shared inbox.

  • Operational analytics

    Getting insight back into the systems where work happens (a reorder point in the purchasing screen, a risk flag in the queue), not just onto a dashboard someone opens on Monday.

Tooling

What we build it with.

No tool here was picked because it was new. Where we do reach for something novel, it is in one place, for a stated reason, and it is written down.

Warehouses
  • Snowflake
  • BigQuery
  • Databricks
  • PostgreSQL
  • ClickHouse
Transformation
  • dbt
  • SQLMesh
  • Spark
  • Airflow
  • Dagster
Ingestion
  • Debezium
  • Fivetran
  • Airbyte
  • Kafka
  • EDI/X12
Consumption
  • Power BI
  • Looker
  • Metabase
  • Evidence
  • Embedded APIs

Questions

Data & analytics, honestly.

  • If reporting queries are slowing your transactional system, or answers require joining more than one source, you need somewhere else to do the work. Below that threshold a read replica and good indexes are cheaper and we’ll say so.

  • Yes. The tool is rarely the problem. We’ve delivered against Power BI, Looker, Tableau, Metabase and plain SQL, and the semantic layer means the choice stops being permanent.

  • Classification at ingest, column-level masking, role-based access enforced in the warehouse, and region-pinned storage where regulation requires it. Access is reviewable and logged.

Further reading

What we think about this, at length.

All posts

Often paired with

Sources

  1. 1.X12 Transaction Sets (opens in a new tab), X12

Next step

Tell us what’s breaking.

Forty-five minutes, no charge, no deck. We’ll tell you what we’d do, what it would likely cost, and whether what you already have can be made to work.

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A person replies, not a sequence: within one business day, from someone who would be on the engagement.