Data Pipeline Engineering

Overview

Data pipelines fail silently. Bad data flows downstream, corrupts reports, poisons ML training sets, and violates compliance requirements — often for days before anyone notices. This skill builds pipelines with the discipline to catch failures at the source.

When to Use

• Before building any ETL/ELT pipeline
• When designing data transformations
• When adding a new data source to a pipeline
• When a downstream consumer reports incorrect data

Process

Step 1: Define the data contract

Before any code: document the contract for every data source:

• Schema (columns, types, nullability)
• Volume expectations (rows per batch/day)
• Freshness SLA (how old can data be?)
• Quality invariants (column X is always positive, column Y is always a valid email)

The contract is your test suite specification.

Step 2: Implement contract validation at ingestion

Validate incoming data against the contract as the first step of every pipeline. Fail loudly on schema violations, out-of-range values, and unexpected nulls. Never silently drop bad records without alerting.

Step 3: Design for idempotency

Every pipeline run must be idempotent: running it twice on the same input produces the same output. Implement with: append-only writes + deduplication, or upserts with stable primary keys, or overwrite semantics with explicit partitions.

Step 4: Implement data lineage

Track: where did each record come from? What transformations were applied? When was it processed? This is required for debugging, compliance (GDPR right-to-erasure), and impact analysis.

Step 5: Design the quality gate

After transformation, before writing to the destination:

• Row count check (within expected range?)
• Key quality metrics (null rates, uniqueness, referential integrity)
• Distribution check (not wildly different from yesterday?)
• Business rule validation (revenue never negative, user_id always set)

Fail the pipeline and alert if quality gates fail.

Step 6: Handle late-arriving and out-of-order data

Define: what is the watermark? How long do you wait for late data? What happens to a record that arrives after the window closes?

Step 7: Design the error handling strategy

For each failure mode:

• Source unavailable → retry with backoff, alert after N failures
• Schema change in source → fail loudly, alert, do not silently drop
• Quality gate failure → fail loudly, quarantine bad data, alert
• Transform error → log with context, route to dead letter queue, alert

Step 8: Implement observability

Track: records in, records out, records failed, processing latency, source freshness, pipeline run duration. Alert on: any of these metrics going outside normal range.

Step 9: Test the pipeline

• Unit tests for each transformation
• Integration tests with representative sample data
• Tests for the quality gate (does it catch bad data?)
• Tests for late-arriving data handling
• Load tests (does it handle 10x volume?)

Step 10: Document the pipeline

For each pipeline: what does it do, what are its inputs, what are its outputs, what are its dependencies, what do you do when it fails? On-call engineers must be able to debug it without the original author.

Anti-Rationalizations

"The source is reliable — we don't need contract validation" Every source eventually sends bad data. The question is whether you catch it.

"Data lineage is a nice-to-have" Data lineage becomes a requirement the first time you need to answer: "which records were affected by this bug?" or "which users' data do we need to delete?"

Verification Requirements

• Data contracts defined for all sources
• Contract validation runs at ingestion
• Pipeline is idempotent
• Data lineage tracked
• Quality gates implemented before destination writes
• Late data strategy defined
• Error handling routes to dead letter queue with alerts
• Observability metrics tracked
• Tests for transformations and quality gates
• Runbook written