Data Engineer with Data Scientist

Rays Techsolutions Inc.

Plano, TX, United States

5 days ago

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Role details

Contract type

Contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Adaptable Database Systems Data Analysis Big Data Code Review Data Architecture Information Engineering Data Governance Data Infrastructure Database Development Design of User Interfaces Apache Hive Standard Sql

+10 more

SQL Databases YAML Data Processing Scripting Freeform SQL Sql Optimization Git Front End Software Development Data Pipelines Databricks

Job description

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., * 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.

Requirements

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., * Cube () 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.
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., 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.

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