ETL / ELT pipelines
Reliable, observable pipelines that move and transform data at any scale.
Reliable, observable pipelines that move and transform data at any scale.
Modern warehouses and lakehouses on Snowflake, BigQuery or Redshift.
Event streaming and CDC for up-to-the-second data with Kafka and friends.
Validation, lineage, cataloguing and access control built in.
Modelled, documented data that's ready for BI and machine learning.
Moving legacy data stacks to scalable cloud-native platforms.
We pressure-test the goal, map the data and define what success measures.
Architecture and approach that's feasible and durable — not just demo-ready.
Senior pods ship working software every week, with you in the loop.
We harden, monitor and optimize for production, then grow with you.
Clean, modelled data that machine learning can actually use.
Quality checks, lineage and access control at every layer.
Architectures that handle growth from gigabytes to petabytes.
What does a data engineering engagement include?
Typically ingestion, transformation, warehousing and streaming, plus the quality, governance and documentation that make the data trustworthy.
Which data platforms do you work with?
Snowflake, BigQuery, Databricks, Redshift and the modern stack around them — dbt, Airflow, Kafka and Spark.
Can you modernize our legacy data stack?
Yes. We regularly migrate legacy warehouses and batch jobs to scalable, cloud-native, real-time platforms.
Tell us what you have in mind — we'll map a path from concept to production and reply within 24 hours.