Data Platform Analyst

The Data Platform Analyst supports operational and financial decision-making by creating dependable datasets, metrics, and pipelines. This role works closely with teams across operations, safety, maintenance, and finance to turn business questions into well-defined requirements and production-ready data solutions using SQL and Python.

What You’ll Do

Business Partnership and Solution Design

• Partner with stakeholders to understand goals, define success criteria, and translate business needs into data requirements (definitions, grain, edge cases, and acceptance criteria).

• Ask clarifying questions early, present options with tradeoffs, and align on the simplest reliable solution.

• Identify opportunities to improve processes, data capture, and metric definitions to reduce downstream confusion.

Data Development (SQL and Python)

• Write and maintain production-grade SQL (queries, views, stored procedures, and functions) to support dashboards, KPI reporting, and operational workflows.

• Use Python for data pipelines, automation, validation, and integration tasks (e.g., scheduled loads, transformations, monitoring, and backfills).

• Optimize and troubleshoot performance issues in SQL workloads and data pipelines.

• Debug data issues end-to-end by reconciling across systems, identifying root causes, and implementing preventative fixes.

Data Quality, Documentation, and Reliability

• Implement data quality checks (completeness, uniqueness, referential integrity, and threshold checks) and automated alerting where appropriate.

• Document datasets and metrics so definitions are consistent and reusable (business rules, lineage, refresh cadence, known limitations).

• Improve maintainability through clean design, modular code, version control practices, and clear operational runbooks.

Integrations and APIs (as needed)

• Work with application owners and vendors to understand source system behavior and data availability.

• Contribute to API-based data ingestion when needed (authentication patterns, pagination, rate limits, and payload validation).

What Success Looks Like

• Delivers data products that stakeholders trust, with clear definitions, stable refresh processes, and documented logic.

• Drives ambiguous requests to resolution by clarifying requirements and proposing solutions.

• Reduces recurring issues through root-cause fixes, monitoring, and data quality checks.

• Communicates changes clearly, including what changed, why it matters, and how results can be validated.

Required Qualifications

• Advanced SQL skills, including complex joins, window functions, CTEs, query optimization, and the ability to read/debug existing SQL code (including stored procedures, functions, and triggers).

• Python proficiency required, including building data pipelines and automation using common libraries (e.g., pandas) and writing maintainable code.

• Ability to translate business needs into technical solutions and drive work through delivery.

• Experience working with structured data models and understanding concepts such as grain, dimensions, and consistent metric definitions.

• Strong communication skills with both technical and non-technical stakeholders.

Preferred Qualifications

• Experience with SQL Server and Microsoft data tooling (or equivalent enterprise data stack).

• Exposure to REST APIs and common integration patterns.

• Experience in operational environments where data supports real-time or near-real-time decisions.

Benefits

We offer a comprehensive benefits package, including health coverage, a 401(k) plan with employer match, and paid time off.