Data Engineering

For teams making decisions on week-old spreadsheet exports

One source of truth, refreshed in minutes

Pipelines, warehouses and dashboards that turn scattered tools into real-time intelligence your leadership trusts.

Pipeline: nightly + streaming
Simulated
Postgres (prod)2 min
Stripesyncing…
HubSpotsyncing…
Salesforcesyncing…
Intercomsyncing…
Warehouse · Snowflake

2,380,000

rows loaded today · dbt models 214

ARR

$4.2M

Churn risk (EU)

▲ 2.1%

Anomaly on churn risk (EU) flagged → report drafted → waiting for the data lead to approve before it posts to #exec-metrics.

Critical-table freshness

< 5 min

Dashboard latency

< 200 ms

Flagged for review

1

Sources unified

15

A human approves every change the AI proposes.

Numbers link to the case study they come from. Company-level numbers are across all engagements.

What we build

Data Engineering, in three parts

Teams whose decisions are blocked by stale dashboards, broken pipelines, or data scattered across 10+ tools.

  1. 01Pipelines
  2. 02Warehouses
  3. 03Analytics
01Pipelines

Ingest, transform, deliver — automatically

Streaming and batch pipelines with scheduling, monitoring and idempotent loads from every source to every destination.

  • Real-time streamingKafka, Kinesis, webhooks and CDC with sub-second latency.
  • Batch orchestrationdbt, Airflow or Python — dependency-aware and observable.
02Warehouses

Modelled for speed and predictable cost

Snowflake, BigQuery or Redshift with clean schemas, incremental materialisations and query optimisation.

  • Schema & modellingDimensional models and analytics-ready marts.
  • Query optimisationClustering, partitioning and cost controls.
03Analytics AI does this

Dashboards that drive decisions

Executive KPIs, drill-downs, anomaly detection and automated alerts so people act on data instead of hunting for it.

  • Executive dashboardsMetabase, Looker, Tableau or custom.
  • Automated reportingScheduled reports and anomaly alerts to Slack or email.
How it runs

Scope in a week, ship in sprints

Every stage produces something you can hand to procurement, audit or your board. Violet is AI-assisted; orange is a human gate.

Stage 01

Data audit & lineage

Inventory every source, every consumer, and every transformation. Identify the data quality, freshness, and lineage gaps causing today's mistrust.

  • Source inventory
  • Lineage map
  • Quality assessment
  • SLA targets
run.log · engagement
 

A human approves every change the AI proposes.

Warehouse / lakehouse. Production-grade Snowflake, BigQuery, Databricks, or Postgres warehouse — tuned for your query patterns and budget.

Ingestion pipelines. Connectors to every source: SaaS APIs, databases (CDC), files, streams, webhooks. Idempotent, observable, alertable.

Transformation layer. dbt project with tested models, documented business logic, and lineage you can show to auditors and analysts alike.

Real-time streaming. Kafka, Kinesis, or Flink pipelines where freshness matters — fraud, ops, customer experience.

BI & semantic layer. Looker, Metabase, or custom dashboards on a tested semantic layer so 'revenue' means the same thing everywhere.

Data quality framework. Tests on schema, freshness, row counts, distributions, and business rules. CI-gated — bad data never reaches production.

Stack

Boring, durable tools by default

Cutting-edge only where it earns its keep. What typically ships in this kind of engagement.

  • Warehouse & lake

    SnowflakeBigQueryDatabricksRedshiftPostgresDuckDB

  • Ingestion

    FivetranAirbyteCustom PythonDebezium (CDC)Estuary

  • Transformation

    dbtSparkPolarsSQLMesh

  • Orchestration & streaming

    AirflowPrefectDagsterKafkaKinesisFlink

  • BI & semantic

    LookerMetabaseHexCubedbt Semantic Layer
Case study
Data platformSeries B SaaS

A unified data platform for a high-growth SaaS company

Real-time ingestion from 15+ sources into a Snowflake warehouse, dbt models and executive dashboards with sub-200 ms queries.

2.4M

Records processed daily

Read the case
“The team went far beyond the initial scope — bringing not just impressive technical execution, but genuine strategic thinking to every decision. They anticipated challenges before they surfaced.”
Kareem Abukhadra

Kareem Abukhadra

Founder, Relentless

Pairs with

What usually ships alongside data engineering

Questions

Before you email us

The things buyers ask on the first call.

Depends on volume, query patterns, existing tooling, and budget. Snowflake for ease + scale, BigQuery for GCP-native shops, Databricks for ML workloads, Postgres if you're under 1TB. We assess in week one.

Scope your Data Engineering project with a senior engineer.

Projects start at $30,000. Typical timeline 6–12 weeks. Reply within 24 hours.

Scope a project
See the case studies

  • 50+

    Production systems shipped

  • 97%

    Client retention

  • 4–8 wk

    Typical time to production

  • 24h

    Reply from a senior engineer