Data Pipelines & ETL Engineering
I build data flows that people can reproduce, reconcile and trust—from raw events to documented analytical models.
What does this work deliver?
Reliable, testable reporting data and ingestion workflows with explicit replay and ownership.
See the implementation in Ecommerce ETL architecture.
What problems does this solve?
When analytics cannot explain where a number came from, building a faster dashboard will not solve the problem. A useful data platform begins with authoritative sources, business definitions, freshness expectations and a plan for replaying failed loads.
Good fit for
- Teams merging application events and operational reporting
- Engineers choosing between scheduled ETL and streaming
- Businesses replacing recurring spreadsheet reconciliation
1. Define sources, identifiers and consumer questions
I identify schema owners, event timestamps and the business keys used to join records. Before selecting tools, I write the checks that would reveal a duplicate, late event or broken source contract. I decide what freshness actually matters for downstream users.
Engineering checks
- Agree metric definitions
- Track event and processing time
- Write source schema and ownership notes
2. Separate ingest, transform and serve
Kafka and Spark can support event streams; Bronze storage preserves replayable inputs. Silver transformations validate and deduplicate; Gold models create stable dimensions and measures. When latency allows it, Airflow can orchestrate hourly or daily transformations separately from ingest.
Engineering checks
- Use checkpoints and idempotent writes
- Design a recoverable backfill
- Verify dimensions and fact-table keys
3. Operate for reliability and cost
I add freshness, row-count and quality signals that show whether reporting can be trusted. A large streaming stack is not automatically the right solution: data volume, team capacity and recovery complexity must justify it.
Engineering checks
- Reconcile warehouse totals to sources
- Monitor failed and stale partitions
- Document ownership and restore procedures
Expected result and engineering trade-off
Reliable, testable reporting data and ingestion workflows with explicit replay and ownership.
Batch processing is often the best choice until a real operational decision demands streaming latency.
Have a backend, data, or product challenge?
Review all service areas, see published work, or contact me about a role or project.
Let’s talk
I like working through complex problems with people who care about the details. Have a product to build, an engineering role, or an interesting challenge? Let’s start a conversation.
SaaimOpen to full-time roles, contract work, and conversations about things worth building.