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The key details from the original listing.
Description supplied by the original job listing.
Title: Data Platform - Production Support Engineer (Databricks, DBT, Snowflake, SQL, Python, Spark, AWS S3, Aurora/MySQL, CDC)
Work Location: 100% Remote
Role Type: Offshore Production Support / On-Call Incident Response
Engagement Model: Part-Time
Work Schedule: Monday–Friday, 4:00 AM–2:00 PM Eastern Time
Technologies: Databricks, DBT, Snowflake, SQL, Python, Spark, AWS S3, Aurora/MySQL, CDC
Role Overview
We are seeking a Data Platform Production Support Engineer with hands-on experience supporting modern data platforms. The ideal candidate will troubleshoot production data pipelines, monitor workflows, investigate failures, recover jobs, execute backfills, and implement minor operational fixes through established CI/CD processes.
This is a shared, part-time production support engagement, not a dedicated full-time position. The engineer will work with the offshore delivery team and coordinate with the onshore lead to ensure timely incident handling, service continuity, and effective knowledge transfer.
Key Responsibilities
Monitor production data pipelines, Databricks Workflows, and PagerDuty alerts.
Investigate and troubleshoot failures across Databricks, dbt, Snowflake, and SQL-based data pipelines.
Perform job recovery, reruns, and backfills to restore normal operations.
Diagnose pipeline failures using Python and Spark where required.
Implement minor operational fixes through existing GitHub, CircleCI, and Databricks Asset Bundles workflows.
Support data pipelines involving AWS S3, Aurora/MySQL, and Change Data Capture (CDC).
Manage incidents from initial investigation through service restoration or a clear handoff to the appropriate engineering team.
Escalate production pipeline issues if a solution is not identified within 15 minutes.
Maintain incident tickets, operational documentation, and runbooks.
Complete end-of-window handoffs with the onshore lead, communicating incident status, outstanding issues, and required follow-up actions.
Required Skills and Experience
Hands-on experience troubleshooting Databricks Workflows, dbt, and Snowflake.
Strong SQL skills for data pipeline troubleshooting and diagnosis.
Working knowledge of Python and Spark for investigating data-processing failures.
Working knowledge of AWS S3, Aurora/MySQL, and CDC-based data pipelines.
Familiarity with GitHub, CircleCI, and Databricks Asset Bundles.
Experience monitoring production workloads, investigating incidents, recovering failed jobs, and executing backfills.
Understanding of production support practices, incident escalation, ticket management, and runbook maintenance.
Ability to work independently, prioritize incidents, and communicate effectively with distributed engineering teams.
Support Schedule and Coverage
Coverage: Monday–Friday, 4:00 AM–2:00 PM Eastern Time.
Coverage model: Shared support availability across the delivery team.
Handoff: End-of-window handoff to the onshore lead.
Monthly operations capacity: 20-30 hours
The selected engineer's allocation will be drawn from this shared monthly capacity. This is not an additional 40-hour allocation or a dedicated full-time position.
Work arrangement
Yes
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