Data engineering for numbers the business can trust.

Move data reliably

Design ingestion, transformation, and migration paths that can be retried, reconciled, and changed without silent downstream damage.

Data Pipelines

The need: The plumbing that moves data between systems, on a schedule or as things happen. When it is fragile, everything downstream inherits that.

Our approach: Pipelines are orchestrated, held in version control, and safe to run again, so a failed load costs a retry and not an evening.

• Orchestration with Airflow, Dagster, or Prefect
• Idempotent and retriable task design
• AWS: Glue, EMR, Step Functions, Lambda, MSK
• Google Cloud: Dataflow, Dataproc, Cloud Composer, Pub/Sub
• Streaming ingestion with Kafka, Kinesis, and Pub/Sub
• Back-pressure, dead letter queues, and replay paths
• Pipeline CI and deployment

ETL and ELT

The need: Turning raw source data into something people can query. This is where the meaning gets added, and where errors slip in quietly.

Our approach: Transformation logic is tested like application code, so a change that would break a number fails in CI and not in a board pack.

• Transformation modelling with dbt
• Incremental models and snapshotting
• Testing: uniqueness, referential integrity, and expectations
• Slowly changing dimensions and historical restatement
• Reusable macros and shared transformation logic
• Environment separation for development and production

Database Migrations

The need: Moving databases between engines, versions, or clouds without losing rows or booking a weekend of downtime.

Our approach: Change data capture keeps both sides in step, so the cutover is short, reversible, and verified before anyone commits to it.

• Homogeneous and heterogeneous engine migration
• Change data capture for continuous replication
• Schema conversion and compatibility assessment
• Row-level reconciliation and parity verification
• Cutover rehearsal and rollback planning
• Managed database targets on AWS and Google Cloud

Modernize the analytical foundation

Move beyond legacy warehouses and one-off reports while keeping definitions governed and reporting available through the transition.

Data Platform Modernization

The need: Coming off a legacy warehouse and a pile of scripts onto something modern, while reporting carries on around you.

Our approach: The new platform runs beside the old one until the numbers agree, and storage is split from compute so query load stops driving the bill.

• Snowflake, BigQuery, Redshift, and Databricks architecture
• Lakehouse table formats: Iceberg, Delta Lake, Hudi
• Legacy ETL decomposition and migration sequencing
• Parallel running and output reconciliation
• Warehouse sizing, auto-suspend, and workload isolation
• Partitioning, clustering, and query cost control

Analytics and Visualization

The need: The reporting people actually open, built on definitions that mean the same thing wherever they turn up.

Our approach: Each metric is defined once in a governed layer, so two dashboards cannot quietly disagree about the same number.

• Semantic and metrics layer design
• Governed metric definitions under version control
• Dashboard modelling and BI delivery
• Self-service analytics enablement
• Report performance and query cost tuning
• Access control, row and column level security

Keep data trustworthy

Put tests, lineage, ownership, and freshness checks inside delivery so issues reach the responsible team before they reach a dashboard.

DataOps

The need: The habits that keep a platform trustworthy: tests, monitoring, and lineage. The alternative is hearing about it from whoever spotted the odd figure.

Our approach: Checks run inside the pipeline, and column level lineage means you know what a change will touch before you make it.

• Data quality testing and expectation frameworks
• Freshness SLAs and volume anomaly alerts
• Schema drift detection and contract enforcement
• End to end column level lineage
• CI and CD for data transformations
• Data quality scorecards and ownership mapping

Related proof

Selected work and engineering blueprints relevant to this practice.

Data platform audit, 1 to 2 weeks

Find where trust, delivery, and cost are being lost.

We trace the path from source to decision, inspect the platform and transformation layer, and identify the failures, bottlenecks, and ownership gaps that should be addressed first.

• Pipeline, platform, and dependency assessment
• Quality, freshness, lineage, and cost risk map
• Prioritized modernization plan and delivery sequence

Inspectable delivery

• Review packs: current state, risks, options, and accountable decisions.
• Decision logs: the chosen path, alternatives, and the reasoning behind it.
• Runbooks: operating, recovery, and handover paths for the team that owns the system.
• Checkpoint baselines: agreed signals that keep delivery progress visible.

Questions worth settling early

• Which data platforms do you work with? Our work includes Snowflake, BigQuery, Redshift, Databricks, modern lakehouse formats, dbt, and orchestration on AWS and Google Cloud. The recommendation follows the workload and operating constraints.
• Can a migration happen while reporting stays live? Yes. Where the source permits it, we use parallel running, change data capture, reconciliation, and rehearsed cutovers to keep reporting available and make rollback credible.
• Do you also build dashboards? Yes, when they are part of a governed analytical system. We focus first on shared metric definitions and access controls so the dashboard does not become another conflicting source of truth.