Data Engineering

The warehouse and pipelines your reporting runs on.

Senior data engineers who re-architect ingestion, modeling, and orchestration in place - deep on Databricks and Snowflake, on AWS or Azure, without taking your daily reporting offline. Named on the contract, reviewed by an Inpro lead.

The brief we usually get

Two problems we are called in to fix.

Concrete starting points, each with the team we field, the stack, and what lands in the first 30 days.

01

Warehouse migration

Typical problemA legacy on-prem warehouse, a 6-hour nightly batch, and reporting nobody dares touch.
What we shipIncremental migration with parallel runs - no big-bang cutover; the old system stays verifiable.
Example stackDatabricksSnowflakedbtAWS
First 30 daysCutover plan agreed and the first domain migrated live, reporting uninterrupted.
02

Pipelines & the dbt layer

Typical problemFragile pipelines, no tests, and models where every metric disagrees with the last.
What we shipA layered dbt project with lineage, tests, and freshness SLAs - observability from day one.
Example stackdbtDatabricksAzurePython
First 30 daysCore marts under test with a passing CI gate and documented lineage.
Deliverables

What you get.

Plain, usable outputs your team can review, run, and maintain after handover.

01 / MapSources and ownership
02 / BuildTested production flows
03 / ProveVisible data quality
Plan

Migration plan

Source systems, dependencies, target architecture, rollout order, and the decision points your team needs to approve.

Why it mattersNo risky big-bang cutover; everyone knows what moves first and why.
Build

Production pipelines

Tested ingestion and transformation jobs delivered in your repositories with clear ownership.

Why it mattersYour team receives working data flows, not a concept deck.
Control

Data quality checks

Freshness, schema, and anomaly checks surfaced where your team can monitor them.

Why it mattersBroken data is caught before it reaches reporting.
Handover

Handover documentation

Runbooks, model conventions, ownership notes, and maintenance guidance for your internal team.

Why it mattersYou are not dependent on us to operate the platform.
Stack we work in

Production tools, not a lab.

Warehouses

SnowflakeDatabricksBigQueryRedshift

Transformation

dbtSQLSparkPython

Orchestration

AirflowDagsterPrefectADF

Streaming

KafkaFlinkKinesisDebezium

Quality

Great Expectationsdbt testsOpenLineage

Infra

TerraformAWSAzureGCP
How we work

From messy sources to owned pipelines.

Each phase leaves a usable asset behind, so the platform improves while the work is still moving.

PHASE 01

Discovery

We map sources, consumers, and the metric you'd measure success against.

Time2 weeks
DeliverableWritten assessment
PHASE 02

Architecture

Modeling, warehouse selection, data contracts, and a governance plan.

Time1-2 weeks
DeliverableTarget design + plan
PHASE 03

Build & cutover

Incremental migration with parallel runs. Something ships every two weeks.

TimeOngoing
DeliverableMigrated domains
PHASE 04

Operate & hand off

Runbooks, dbt conventions, lineage docs. Your team owns it, we stay reachable.

TimeMonth 4+
DeliverableOwnership handover
Who delivers this

Named engineers from the bench

A snapshot. You interview the actual people before anyone joins your team.

ArbenData engineer
Available Aug

Senior Data Engineer

9 yrs · warehouse & pipelines
dbtSnowflakeAirflowPython
EnglishC1
GermanB2
InterviewsCleared · 2 clients
EliraAnalytics engineer
Available now

Analytics Engineer

6 yrs · semantic layer & dbt
dbtBigQueryLookerSQL
EnglishC1
GermanB1
InterviewsReady to interview
Request a shortlist

Send us the data role. We'll return a named shortlist.

Tell us the stack and the timeline. Within 5 business days you get 2-4 named engineers with CVs and rates from 290 - 380€/day, to interview yourself.