Data engineer with Data scientist
Client Full Address: Mountain view, CA (Onsite)
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.
What Success Looks Like
• Reliable datasets and pipelines that support downstream reporting and applications.
• Efficient SQL queries and scalable data models.
• Independent investigation and clear explanation of reporting discrepancies.
• Consistent metrics across data sources and reporting layers.
• Clear documentation for UI developers and other data consumers.
• Improved reporting performance and maintainability.
Ideal Candidate Profile
We seek a technically strong, detail-oriented problem solver comfortable at the intersection of SQL development, data
engineering, and analytics. The ideal candidate can independently investigate complex data issues, understand data
architecture, and build dependable solutions. While Cube and call center reporting familiarity are highly valued, deep
SQL expertise and strong data engineering or data investigation experience are the highest priorities.
