Key Responsibilities
• SQL Development & Optimization: Write, maintain, and optimize complex SQL queries using CTEs, window functions, joins, and aggregations.
• Data Engineering & Pipelines: Build, schedule, monitor, and maintain reliable data pipelines for accurate and consistent reporting.
• Data Modeling & Table Design: Design analytical tables with appropriate partitions, keys, and levels of granularity.
• Data Investigation: Trace metrics to source systems, identify root causes, and reconcile discrepancies between reports.
• Reporting & Semantic Layer: Support measures, dimensions, aggregations, and consistent reporting across applications.
• Data Governance: Navigate table-access requests and handle restricted data appropriately.
• Collaboration & Documentation: Partner with UI developers and stakeholders; document datasets and use Git, pull requests, and code reviews.
Required Qualifications
• Strong advanced SQL skills, including CTEs, window functions, complex joins, and aggregations.
• Solid data engineering experience with pipelines, transformations, scheduling, and data quality.
• Experience designing analytical tables, including partitioning, keys, and data granularity.
• Ability to investigate data issues, reconcile reports, and perform root-cause analysis.
• Experience working with large datasets and analytical data platforms.
• Familiarity with access permissions, data governance, and restricted datasets.
• Working knowledge of Git, pull requests, and collaborative development.
• Basic Python scripting for automation, data manipulation, or troubleshooting.
• Strong communication skills and clear technical documentation for UI and engineering teams.
Preferred Qualifications
• Cube (Cube.dev) experience is a significant plus: defining measures and dimensions in YAML; understanding
rollups and pre-aggregations; troubleshooting when queries do not use pre-aggregated data.
• Hands-on experience with Databricks and Spark SQL.
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• Familiarity with call center/contact center data and reporting is a plus, including call volume, average handle time
(AHT), service levels, agent performance, and customer interactions.
• Experience with operational dashboards, KPI reporting, and performance analytics, ideally in a call center or customer
support setting.
• Familiarity with semantic data modeling and analytics infrastructure.
• Experience working closely with frontend/UI engineering teams.
