AWS data engineering for marketing and commerce: S3 and Glue pipelines, Redshift and Athena, Kinesis event streams, and infrastructure as code.
Fifteen services, no pipeline
AWS gives you every component and no opinion about how to assemble them. The usual outcome is a data layer that grew by accident: a Lambda someone wrote two years ago, a Glue job nobody will touch, CSVs in three S3 buckets with different naming conventions, and no lineage from source to dashboard. It technically works, no single person understands all of it, and the first serious failure is an archaeology project.
What we build on AWS
- S3 data lake structure with sane partitioning, lifecycle policies, and table formats
- Ingestion with Glue, Lambda, or containerized jobs depending on what the workload actually needs
- Kinesis or MSK streaming pipelines where events genuinely need to arrive continuously
- Query and warehouse layer on Athena, Redshift, or Redshift Serverless, sized to your usage
- Orchestration with Step Functions or managed Airflow, with retries and alerting
- Everything defined in Terraform or CDK so environments are reproducible, not hand-built
How we work
Document what exists today and what reporting depends on it before changing anything
Establish lake structure and IAM boundaries as the foundation
Rebuild the highest-value pipeline as code, with tests and monitoring, as the pattern to follow
Migrate remaining pipelines onto that pattern and decommission the orphans
Typical stack
Frequently asked questions
Athena is ideal for intermittent querying over S3 with no cluster to manage and pay-per-query cost. Redshift Serverless suits consistent BI workloads that want warehouse semantics without capacity planning. Snowflake runs on AWS and is worth the extra vendor when you need its multi-tenant access controls or cross-cloud portability. We size all three against your real query patterns.
Batch is enough far more often than teams assume, and it is dramatically cheaper to run and debug. Streaming is justified when a decision genuinely depends on sub-minute freshness, such as real-time bidding signals or fraud checks. Hourly batch satisfies most marketing reporting, since the upstream platforms do not update faster than that anyway.
Because hand-built AWS environments cannot be reliably recreated, reviewed, or rolled back. With Terraform or CDK, staging matches production, changes go through review, and recovery is a re-apply rather than a reconstruction from memory.
Yes, and that is the normal case. We start by documenting what runs today and what depends on it, then improve incrementally. Wholesale replacement is rarely warranted and always riskier than it looks.
Go deeper
Rebuild your AWS data layer
Tell us what runs on AWS today and we will map what to keep, what to rebuild, and in what order.
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