Job Description

Job Responsibilities:

• Own the day-to-day health of the Enterprise Support data layer: pipelines, Hive tables, SQL, and dashboards.
• Design and build data models for new reporting needs, proposing the approach rather than receiving a specification.
• Maintain a documented source of truth for Enterprise Support metrics: definitions, ownership, and freshness.
• Apply AI and LLM tooling to accelerate data work and to extract signal from unstructured support data such as ticket notes and resolution summaries.
• Improve pipeline reliability and reduce manual effort over time through automation.
• Monitor pipeline health, resolve failures and data quality issues, and run backfills.
• Deliver recurring reporting (weekly, monthly, quarterly) and ad hoc analysis.
• Apply and maintain standards for naming, documentation, and schema change management.
• Proactively surface data quality, governance, and coverage gaps.

Skills:

• Advanced SQL, ideally with Presto and Hive against large datasets.
• Building and maintaining production data pipelines: scheduling, dependency management, backfills, and monitoring.
• Data modeling and warehouse design: schema design, grain, partitioning, and documentation.
• Practical use of AI and LLM tooling in data work, and judgment about where it is and is not appropriate.
• Data governance practice: documented metric definitions, clear table ownership, schema change management, and retiring stale datasets.
• Python or similar scripting for data transformation and automation.
• Dashboarding and data visualization.
• Prior experience supporting an IT, enterprise support, or operations organization.
• Self-directed: able to take a loosely defined problem, propose an approach, and execute it.
• Bachelor's degree in a quantitative, analytics, computer science, or information systems field, or equivalent practical experience.

Education/Experience:

• 6-7 years of experience in data analytics, business analytics, or analytics engineering.
• Advanced SQL skills, including experience with large scale distributed query engines such as Presto and Hive.
• Demonstrated ownership of production data pipelines and data models, including design decisions rather than only maintenance.
• Practical experience applying AI or LLM tooling within data workflows.
• Experience with data modeling and dashboard development.
• Ability to work independently and communicate status, recommendations, and blockers clearly in writing.
• Data governance experience: metric catalogs, lineage, table ownership, and schema change management.
• Experience supporting an IT, enterprise support, or technical operations organization.
• Python or similar scripting experience for data automation.
• Experience maintaining operational metrics and SLA reporting.

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