(Data engineering)

Data engineering that gives every report the same answer

Ingestion, ELT, a warehouse worth querying and the tests that stop bad rows reaching a dashboard. Data engineering is plumbing, and plumbing gets judged on whether anyone has to think about it.

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Data engineering

(How pipelines actually fail)

Pipelines rarely fail loudly. A source system adds a column, a currency arrives as a string, a job half-finishes at three in the morning, and the Monday report is quietly wrong for a fortnight until somebody in finance notices. Most of the engineering here exists to make that noisy instead of silent.

Freshness checks, row-count expectations, a contract with each source owner, and an alert naming the affected table rather than the DAG. Dull work, and the reason people stop keeping private spreadsheets.

(What we deliver)

What data engineering services include

Usually delivered as one platform build, then extended source by source as the business asks for more.

  1. Managed connectors through Fivetran or Airbyte where they exist, change data capture with Debezium for operational databases, and written connectors for the APIs nobody supports. Raw data lands untouched so it can always be replayed.

  2. Snowflake, BigQuery or Databricks chosen on your workload and your existing cloud, not on preference. Table design, schemas by environment, Iceberg or Delta where open formats matter, and role-based access from day one.

  3. Staging, intermediate and mart layers with tests, documentation and lineage generated from the code. Business logic lives in version control where it can be reviewed, rather than in a view somebody edited in the console.

  4. Airflow or Dagster with idempotent tasks, sensible retries, and backfills that can be rerun without duplicating rows. Schedules are driven by data arrival where possible instead of a hopeful cron time.

  5. An agreed schema with each source team, checks on freshness, volume, uniqueness and accepted values, and a rule about what happens on failure: block the load, or quarantine the rows and carry on.

  6. Moving off SSIS, hand-rolled scripts or an ageing Redshift cluster in stages, running old and new in parallel until the numbers match. Warehouse spend is attributed per model so the expensive job is identifiable.

(Usual stack)

(How we work)

How the work runs

Every engagement is scoped in phases, priced per phase, and reviewed with you at the end of each one.

  1. 01

    Discovery

    We map the problem, the systems around it and what a good outcome looks like, then scope the work in phases you can stop after.

  2. 02

    Design

    Flows, architecture and interfaces agreed before anyone writes production code.

  3. 03

    Build

    Two-week increments, a working environment you can open, and a demo at the end of each one.

  4. 04

    Testing

    Functional, performance, security and accessibility checks run through the build, not bolted on at the end.

  5. 05

    Launch

    Deployment, monitoring, documentation and the handover your team needs to run it.

  6. 06

    Support

    Fixes, updates and the next set of features, at an agreed monthly capacity.

(Why Team of Keys)

What we do differently in data engineering

A warehouse is easy to stand up and hard to keep trustworthy. These practices are aimed squarely at the second problem.

  1. 01

    A contract with each source

    Source owners agree the schema and the notice period for changes. When a field disappears anyway, the test catches it at load time and names the team who changed it.

  2. 02

    Tests that block

    Critical checks stop the pipeline rather than emailing a warning nobody reads. Lesser checks quarantine rows for review, so one bad batch does not hold up the whole load.

  3. 03

    Backfills that are safe to repeat

    Every task is idempotent and keyed, so rerunning three months of history produces the same table rather than triplicate rows. That property is tested, not assumed.

  4. 04

    Lineage anyone can read

    Generated documentation shows which source feeds which mart and which dashboard, refreshed on every deployment. When a figure looks wrong, tracing it back to raw data takes minutes instead of an afternoon.

  5. 05

    Cost attributed per model

    Warehouse credits are tagged by transformation and reported monthly, so you can identify the model that quietly costs more each month than the question it answers is worth to anybody.

(Related)

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(FAQ)

Questions, answered

A first platform with a few sources is quoted as one phase, with additional sources priced individually once the pattern is established. Running cost is separate and belongs to your cloud account: warehouse compute, storage and connector licences. Estimates for those are produced during design, before you commit to a vendor.

BigQuery suits teams already in Google Cloud with bursty analytical queries. Snowflake fits mixed workloads and a large number of separate teams. Databricks earns its place where Spark, notebooks and machine learning sit alongside the warehouse. The choice is made in design with your workload and existing cloud commitments in front of us.

If more than one person writes transformations, yes. It brings version control, tests, environments and generated lineage to SQL that would otherwise live in scattered views. For a single analyst with three tables it is overhead, and we will say so.

Usually. Existing SSIS packages, stored procedures or Python scripts get reviewed and kept where they work. Migration happens source by source with both versions running until the outputs reconcile, so nothing is switched off on the strength of a promise.

Design takes two to three weeks. A first warehouse covering two or three core sources, modelled in dbt with tests and a scheduled load, typically lands six to ten weeks after that. Additional sources are quicker once the ingestion and testing patterns exist.

(Global presence)

Nine countries, one studio behind them.

Every project is designed, built and shipped from one studio.
Turn the globe, or pick a country to see what we deliver there.

NoidaDrag to turn

Studio · Noida, India · --:--

(Next step)

Send us the report nobody trusts

Tell us which systems feed it, how often it breaks, and what it is used to decide. You get a platform design and a price per phase.

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Or write to info@teamofkeys.com · Noida, India