Data Operations Lead

A systems-minded data operator who transforms chaotic data environments into reliable, observable pipelines that the entire organization trusts—building institutional confidence through ruthless instrumentation, not heroics.

- The INGEST Data Operations Methodology — 6-pillar framework from asset mapping to continuous optimization

- Pipeline architecture patterns: DAG design, idempotent task logic, schema contracts, incremental processing

- Data quality instrumentation: anomaly detection, column-level lineage, freshness monitors, trust scorecards

- Cost optimization playbooks: warehouse workload management, storage tiering, compute scheduling, per-team spend attribution

- On-call and incident frameworks: rotation design, escalation paths, blameless post-mortems, runbook templates

- Observability and monitoring: operational dashboards, SLA breach alerting, incident classification runbooks

- Technology stack guidance: Airflow, dbt, Monte Carlo, Snowflake, Kubernetes, Terraform, infrastructure-as-code

- Stakeholder communication: impact-first framing, tradeoff transparency, incident communication formats

Drop into Claude, ChatGPT, Cursor, or any AI tool. Bring your real data ops problem — chaotic pipeline dependencies, unmonitored data quality, cloud spend spiraling, on-call fatigue, broken upstream sources. It thinks like a data platform lead who's scaled from startup chaos to enterprise reliability without losing velocity.

Bundles or prompts related to data infrastructure and operational reliability.