Data Engineer with Data Scientist experience

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.

2

• 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.