vaquarkhan Using Data Engineering Agent Skills
Overview
Start here before changing code, SQL, orchestration, contracts, or infrastructure. This skill maps the user request to the right data engineering workflow so the agent does not skip specification, quality, governance, replay, or rollback thinking.
When to Use
- starting a new session
- deciding which skill should lead execution
- translating a vague request into the right workflow
- choosing the best preset, starter pack, or runnable example
- deciding the safest next command
Do not stop here once the task has been classified. Load the actual execution skill after triage.
Workflow
Classify the task type.
- New data product, new pipeline, or major behavior change: use
data-specification - Approved scope that needs sequencing: use
pipeline-planning-and-task-breakdown - Implementation across code, SQL, or jobs: use the relevant build skill plus a matching preset
- File drops,
SFTP, or partner-managed feeds: usefile-and-partner-feed-ingestion Glue Data Catalog,Lake Formation, or AWS-native catalog governance: useglue-data-catalog-and-lake-formation-governancePythonimplementation work: usepython-data-engineering-and-pipeline-packagingScalaJVM data jobs: usescala-data-engineering-on-jvm-runtimesJavaconnectors or data services: usejava-data-engineering-and-integration-servicesUnity Catalogor Databricks lakehouse governance: useunity-catalog-and-lakehouse-governancePurviewor Azure-native governance: usemicrosoft-purview-and-azure-data-governanceDataplex, policy tags, orBigQuerygovernance: usedataplex-and-bigquery-governanceMySQLversusNoSQLor operational-store choice: useoperational-datastore-selection-relational-and-nosqlETL,ELT, or transformation-boundary redesign: useetl-elt-and-modernization-strategy- Test data generation, seeded fixtures, or lower-environment realism: use
test-data-preparation-and-synthetic-data - Production-like data refresh into development, QA, or staging: use
lower-environment-data-masking-and-obfuscation - Quality, reconciliation, or contract correctness: use
data-quality-and-contract-testing - Resiliency testing, failure injection, restart drills, or failover validation: use
data-resiliency-testing-and-failure-injection - Disaster recovery or business continuity planning: use
data-platform-disaster-recovery-and-business-continuity - Reliability issue, incident, or broken publish: use
incident-triage-and-pipeline-recovery - Replay, rerun, cutover, or historical repair: use
safe-backfill-and-replay-orchestration,orchestration-and-backfills, anddata-migration-and-platform-cutover - Serverless Spark on Lambda or short-lived runtimes: use
spark-serverless-reliability-and-state-management - Kafka production guardrails, DLQs, or schema enforcement: use
kafka-resilience-and-schema-evolution - Live lag, Spark plans, or run-state diagnosis via MCP: use
mcp-data-observability-integration - Governance, lineage, privacy, or access changes: use
lineage-pii-and-governance PII,PCI,HIPAA,PHI, or audit-bound data handling: usedata-security-compliance-and-regulated-data- region-specific compliance, sovereignty, or localization rules: use
regional-data-compliance-and-sovereignty - sustainability or
ESGreporting data products: useesg-and-sustainability-regulatory-reporting - Schema changes or breaking contracts: use
schema-evolution-and-contract-migrations Informatica,Talend, or legacy ETL modernization: useenterprise-etl-and-data-integration-modernization- mainframe data offload or modernization: use
mainframe-modernization-and-data-offload - Platform-team ownership, golden paths, or support-boundary design: use
data-platform-operating-model-and-service-ownership - quality tool selection, rule severity, or quality operating model: use
data-quality-platforms-and-rule-management
- New data product, new pipeline, or major behavior change: use
Choose the execution surface.
- Python pipeline or validation package:
python-data-engineering-and-pipeline-packaging - Scala Spark or JVM stream job:
scala-data-engineering-on-jvm-runtimes - Java connector or metadata service:
java-data-engineering-and-integration-services - Warehouse/dbt work:
warehouse-and-schema-design+dbt-and-analytics-engineering - Snowflake-native warehouse pipelines:
snowflake-native-pipelines-and-governance - BigQuery or Dataform platform workflows:
bigquery-and-dataform-platform-engineering - Platform-native governance on
AWS,Azure,GCP, orDatabricks: use the matching governance skill plus the matching platform preset - Spark or distributed processing:
spark-and-distributed-processing - Pipeline scheduling, DAG behavior, or event-driven orchestration:
airflow-and-workflow-orchestration - Streaming or messaging:
streaming-and-messaging-systems - CDC and source replication:
cdc-and-incremental-loadingordebezium-and-kafka-connect-cdc - Lakehouse table work:
lakehouse-table-format-engineeringordelta-lake-and-medallion-architecture - Infra and environment setup:
terraform-and-data-platform-infrastructure - Multi-jurisdiction governance and reporting:
regional-data-compliance-and-sovereigntyoresg-and-sustainability-regulatory-reporting - Enterprise ETL estates:
enterprise-etl-and-data-integration-modernization
- Python pipeline or validation package:
Choose the platform preset.
- AWS-heavy platform:
aws-data-engineering - Azure-heavy platform:
azure-data-engineering - GCP-heavy platform:
gcp-data-engineering - Databricks-first platform:
databricks-lakehouse-engineering - Snowflake-centric warehouse platform:
snowflake-modern-data-platform - Informatica-centered estate:
informatica-data-integration - Talend-centered estate:
talend-data-integration - Mixed platform or migration state:
multi-cloud-hybrid-data-engineering - Apache-first stack: load the matching Apache preset for Spark, Flink, Airflow, Kafka, or Iceberg
- AWS-heavy platform:
Recommend the fastest bootstrap asset.
- New pipeline: start with
templates/source-contract.yamlortemplates/dataset-contract.yaml - Metrics or semantic work: start with
templates/metric-contract.yaml - Operational recovery or release work: start with
templates/incident-runbook.md,templates/backfill-plan.yaml, ortemplates/release-gate-evidence.yaml - Schema migration work: start with
templates/schema-change-plan.yaml - Greenfield lakehouse adoption: use
starter-packs/aws-lakehouse-starter.yaml - Medallion delivery: use
starter-packs/databricks-medallion-starter.yaml - Warehouse modeling: use
starter-packs/warehouse-analytics-starter.yaml - Streaming reliability: use
starter-packs/streaming-reliability-starter.yaml - Production reliability and failure recovery: use
starter-packs/production-reliability-starter.yaml - Data-platform release design: use
starter-packs/data-platform-cicd-release-starter.yaml - Resilience drills and recovery proof: use
starter-packs/resiliency-testing-starter.yaml - Validation and security review: use
starter-packs/validation-security-review-starter.yaml - Privacy or governance work: use
starter-packs/privacy-governance-starter.yaml - Regional compliance or ESG reporting: use
starter-packs/regional-compliance-and-esg-reporting-starter.yaml - Test data and lower environments: use
starter-packs/test-data-lower-environments-starter.yaml - Enterprise ETL modernization: use
starter-packs/enterprise-etl-modernization-starter.yaml
- New pipeline: start with
Recommend an example project when concrete context helps. Prefer runnable scaffolds when the team needs executable proof; use architecture blueprints for
/specand/planonly. Seeexamples/README.mdfor the full type column.Runnable:
- Warehouse/dbt:
examples/dbt-warehouse-marts/ - Databricks medallion:
examples/databricks-delta-medallion/ - AWS lakehouse:
examples/aws-s3-glue-athena-iceberg/ - Kafka and Flink:
examples/kafka-flink-streaming/ - AWS serverless Spark and MSK reliability:
examples/aws-serverless-spark-msk-reliability/
Blueprint:
- API or SaaS ingestion:
examples/api-saas-to-warehouse-ingestion/ - Privacy workflows:
examples/privacy-retention-deletion-workflow/ - Platform cutover:
examples/multi-cloud-warehouse-cutover/
- Warehouse/dbt:
Choose the safe next command.
- unclear intent or missing contracts:
/spec - approved scope but too much ambiguity in sequencing:
/plan - implementation work with clear acceptance criteria:
/build - quality, reconciliation, or release gating:
/validate - reliability, governance, or cost review:
/review - replay, repair, or cutover work:
/backfill - deployment or publish readiness:
/ship
- unclear intent or missing contracts:
Pull in supporting references only when needed.
- ingestion:
references/api-saas-ingestion-checklist.md - file feeds:
references/file-ingestion-checklist.md - quality:
references/data-quality-checklist.md - security and compliance:
references/security-compliance-regulated-data-checklist.md - asset discovery and install surfaces:
registry/assets.json - testcase design:
references/data-validation-and-testcase-patterns.md - resiliency testing:
references/data-resiliency-testing-patterns.md - disaster recovery:
references/data-platform-dr-bcp-checklist.md - operating model:
references/data-platform-operating-model-checklist.md - platform-native governance:
references/platform-native-governance-patterns.md - platform security:
references/data-platform-security-checklist.md - anti-pattern review:
references/data-engineering-anti-patterns.md - ETL and ELT strategy:
references/etl-elt-modernization-checklist.md - mainframe modernization:
references/mainframe-modernization-checklist.md - progressive release:
references/progressive-data-release-patterns.md - test data:
references/test-data-preparation-checklist.md - lower environments:
references/lower-environment-masking-checklist.md - enterprise ETL modernization:
references/enterprise-etl-modernization-checklist.md - regional compliance:
references/regional-compliance-and-data-sovereignty-checklist.md - ESG reporting:
references/esg-and-sustainability-reporting-checklist.md - streaming architecture:
references/streaming-architecture-patterns.md - serverless Spark reliability:
references/spark-serverless-reliability-patterns.md - Kafka production guardrails:
references/kafka-production-guardrails.md - MCP observability:
references/mcp-data-observability-patterns.md - cloud-specific common architecture patterns:
references/cloud-data-engineering-architecture-patterns.md - pipeline orchestration across schedulers and clouds:
references/pipeline-orchestration-patterns.md - data-quality tool boundaries and severity design:
references/data-quality-tooling-and-rule-management.md - reference selection:
references/README.md - streaming:
references/streaming-checklist.md - recovery:
references/incident-recovery-checklist.md - schema change:
references/schema-migration-checklist.md - observability:
references/observability-and-sla-checklist.md
- ingestion:
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "It is only a small pipeline tweak." | Small data changes still break dashboards, contracts, or downstream jobs. Classify the work correctly first. |
| "We already know the stack." | Stack knowledge does not replace contract, replay, quality, or publish decisions. |
| "We can skip the example because the work is custom." | Examples reduce guessing and make rollout patterns safer even when the domain differs. |
| "Backfill is just rerunning the job." | Replay work can amplify bad data and downstream impact if windowing and reconciliation are unclear. |
Red Flags
- the agent starts coding before choosing a workflow skill
- stack-specific implementation starts without a matching preset
- a replay or schema change is planned without explicit validation gates
- the agent loads the full repository instead of the smallest useful bundle
- there is no named next command for the current task
Verification
- The task type has been mapped to the correct workflow skill
- A matching platform preset has been chosen where relevant
- A starter template, starter pack, or example has been recommended when useful
- The safest next command has been identified
- Quality, governance, replay, and rollback risks were not skipped during triage