Data Platform Analyst

Responsibilities

• 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.

Requirements

• 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

Additional Information

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.

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Company Intel

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