Data Engineering
Pipelines you can trust at 3 a.m.
Most data problems start at ingestion. We design and build pipelines that are idempotent, tested and monitored, so your analysts and models work from data that arrives on time and means what it says.
Sound familiar?
The problems we solve
Fragile pipelines
Jobs fail silently overnight and dashboards show yesterday's numbers, or worse, wrong ones.
Hand-built scripts
Critical logic lives in scripts only one person understands, with no tests or version control.
Slow onboarding of sources
Every new system takes months to integrate because nothing is reusable.
Rising cloud bills
Full reloads and oversized clusters run where incremental processing would do.
Outcomes
What you get
- Fresh, complete data delivered on an agreed schedule
- Fewer broken dashboards and late-night incidents
- Pipelines your own team can understand and extend
- Lower compute cost through incremental, right-sized processing
Approach
How we deliver it
- 1
Map sources and consumers
Inventory systems, data contracts, freshness needs and owners.
- 2
Design patterns once
Reusable ingestion, transformation and testing templates for every domain.
- 3
Build in increments
Deliver one domain end to end, prove it in production, then scale the pattern.
- 4
Operate and hand over
Alerting, runbooks and pairing sessions so your team owns it.
Deliverables
What we hand over
Working software, tests and documentation in your repositories and cloud accounts.
Technologies
- Apache Spark
- dbt
- Apache Airflow
- Dagster
- Apache Kafka
- Debezium
- Fivetran
- Python
- SQL
- Source integration
- Batch, change data capture (CDC) and streaming ingestion from databases, SaaS apps, files and event streams.
- Transformation layer
- Modular, version-controlled transformations with dbt or Spark, reviewed like application code.
- Orchestration
- Scheduled and event-driven workflows with retries, backfills, alerting and clear ownership.
- Data observability
- Freshness, volume and schema monitoring with alerts routed to the right team.
- Documentation
- Lineage, runbooks and onboarding notes handed over with the code.
Use cases
Where this makes a difference
Real-time order and inventory feeds
Kafka and CDC streams keeping stock levels accurate across channels within seconds.
Finance data hub
Daily, reconciled ledgers and sub-ledgers feeding close, reporting and planning.
Customer 360
CRM, web, support and billing data unified into one tested customer model.
FAQ
Common questions
Do you work with our existing tools?
Yes. We start from what you already run and only recommend changes where they remove real cost or risk.
Batch or streaming?
Whichever the use case needs. Most estates need both; we design them to share one set of tested transformations.
Who maintains the pipelines afterwards?
Your team, if you want. We document everything and pair with your engineers, or we can operate the platform for you.
Related services
All services →ETL & Data Integration
Integrate every system and modernize legacy ETL (Informatica, SSIS, DataStage, Talend) into maintainable, cloud-native ELT pipelines.
Modernize ETL to cloud-native ELT
Big Data Migration
Move Hadoop, Teradata, Netezza, Exadata and on-premises warehouses to a cloud lakehouse safely, with parallel runs and zero-surprise cutovers.
Hadoop and legacy warehouses to the cloud
Data Platform Modernization
Lakehouse and cloud data platforms on Databricks, Snowflake, Microsoft Fabric or BigQuery, designed to serve analytics and AI from one governed copy of data.
Lakehouses on Databricks, Snowflake, Fabric
Tell us where your data is today.
A 45-minute conversation with a senior engineer. You leave with an honest view of what to build first, whether or not we work together.