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d-padmanabhand-padmanabhan/agent-engineering-handbook/skills/data-engineering/SKILL.md

data-engineering

Skill17 starsChanged 55 days agoMITView on GitHub

Data engineering workflows for designing and reviewing batch/streaming pipelines across Databricks, Snowflake, Confluent Kafka, and Teradata (contracts, backfills, quality, governance, cost, observability). Use when the user mentions data engineering, ETL/ELT, lakehouse/warehouse, Kafka, Databricks, Snowflake, or Teradata.

21 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 18 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill to:

    line 10
  2. Very specific:

    When relevant, apply these rules (in addition to 475-sql.mdc and 480-data-engineering.mdc):

    line 62

“Common” is measured: kept by at least 6 in 10 of the 18 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Data Engineering

Scope

Use this skill to:

  • Review data pipeline PRs (batch or streaming)
  • Design new datasets, ingestion pipelines, and transformations
  • Plan backfills/replays safely
  • Apply platform-specific guidance for Databricks, Snowflake, Confluent Kafka, and Teradata
  • Route cross-service Outbox/Inbox and Saga consistency design to the distributed transactions skill (${HANDBOOK_ROOT}/skills/distributed-transactions/SKILL.md)

Core principles (defaults)

  • Idempotent: safe to re-run for a given window/offset
  • Contract-driven: schema + semantics + SLA are explicit
  • Observable: each run emits counts, timings, and progress/watermarks
  • Governed: least privilege, masking/row filtering for sensitive data
  • Cost-aware: incremental + pruning by default; avoid full scans

Quick start: PR review workflow (local)

  1. Determine base branch (usually main).
  2. Collect git facts:
git branch --show-current
git log main..HEAD --oneline
git diff --name-status main...HEAD
git diff --numstat main...HEAD
git diff main...HEAD
  1. Review using the structure below.

Review output format

  • Critical: correctness, data loss/duplication, security/PII leaks, breaking contracts
  • Recommended: performance/cost risks, operational gaps, maintainability
  • Optional: style, naming, documentation improvements

Quick start: design workflow (new pipeline / dataset)

Produce a short design covering:

  • Inputs: sources, formats, volumes, SLAs
  • Contract: schema, keys, semantics (event vs processing time), evolution policy
  • Processing: batch vs streaming, watermarking/offset tracking, dedupe/upsert strategy
  • Outputs: layers (raw/curated/serving), consumers, downstream blast radius
  • Quality: freshness/volume/uniqueness checks, quarantine strategy
  • Security: PII classification, masking/row filters, least privilege
  • Ops: alerting, retries, DLQ/quarantine, runbook for backfills
  • Cost: partitioning/pruning, incremental strategy, warehouse sizing (if relevant)

Platform-specific rule pointers

When relevant, apply these rules (in addition to 475-sql.mdc and 480-data-engineering.mdc):

  • Databricks: rules/481-databricks.mdc
  • Snowflake: rules/482-snowflake.mdc
  • Kafka / Confluent: rules/483-kafka.mdc
  • Teradata: rules/484-teradata.mdc

References

  • references/contracts-and-evolution.md
  • references/backfills-and-replays.md
  • references/databricks.md
  • references/snowflake.md
  • references/kafka-confluent.md
  • references/teradata.md
  • Distributed transactions (${HANDBOOK_ROOT}/skills/distributed-transactions/SKILL.md)

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