Data Platform Analyst

Groendyke Transport•Enid, OK

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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).