• Provide timely, well-documented responses to data questions from stakeholders across Engineering, Marketing, Business, Data Science, and Analytics, escalating when appropriate and keeping requesters informed. • Take on well-scoped, small data modeling requests in our warehouse: update or add dbt models and tests, write clear model documentation, and open PRs with thoughtful descriptions and checks. • Support the health of our data platform by pairing with the on-call engineer to monitor job runs and alerts, triage issues, and execute playbooks; escalate quickly when outside scope. • Proactively monitor nightly and hourly pipelines (e.g., Airflow DAGs, dbt Cloud runs, AWS DMS replication tasks) and remediate common failures (retries, backfills, simple config fixes) under guidance. • Help maintain data reliability by responding to Monte Carlo observability alerts, validating anomalies, and creating or refining data quality tests. • Partner with engineers to simplify and automate routine remediation steps; contribute small improvements that make our pipelines more robust and observable. • Document what you learn: improve runbooks, add troubleshooting steps to our internal knowledge base, and keep tickets updated with clear status and outcomes.