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Location: Rancho Cordova, CA
Job Type: Contract
Work Mode: Onsite / As applicable
We are looking for an experienced Talent Acquisition Data Engineering & Analytics Specialist to build and deliver a production-ready reporting and analytics solution for Talent Acquisition.
The ideal candidate will own the complete data lifecycle, from raw recruiting data ingestion to automated, leadership-facing Power BI dashboards. This includes establishing cloud data warehouse connectivity, developing automated ETL/ELT pipelines, designing dimensional data models, and building a comprehensive Power BI dashboard suite.
This is a technical data engineering and analytics role and does not involve operational recruiting activities.
Establish and validate connections between Talent Acquisition source systems such as SmartRecruiters, Workday, Jira, and ServiceNow.
Build and maintain production-grade ETL/ELT pipelines, including API-based data ingestion.
Configure Power BI connectivity and scheduled refresh automation.
Implement data quality checks, refresh monitoring, and failure-handling mechanisms.
Design and deploy dimensional data models supporting requisition, recruitment funnel, and hiring analytics.
Develop and optimize complex SQL queries and transformations.
Build historical and point-in-time reporting, including requisition status history and pipeline snapshots.
Define table grain, conformed dimensions, star schemas, and slowly changing dimensions (SCD).
Develop comprehensive Power BI dashboards covering:
Executive Summary
Requisition & Pipeline Health
Funnel Conversion
Time-to-Fill & Time-in-Stage
Offer Metrics
Sourcing Effectiveness
Recruiter Workload
Talent Acquisition Operational SLA Metrics
Build governed semantic models and certified DAX measures.
Implement Row-Level Security (RLS).
Optimize Power BI model performance for large datasets.
Publish and manage dashboards through Power BI Service.
Document pipeline architecture, data lineage, model diagrams, and measure definitions.
Develop refresh, monitoring, and troubleshooting runbooks.
Establish consistent metric definitions in collaboration with stakeholders.
Create end-user guides and conduct enablement sessions for business users.
Deliver complete technical documentation and knowledge transfer.
Partner with Talent Acquisition leadership to understand reporting requirements.
Translate business questions into measurable analytics solutions.
Present dashboard walkthroughs and analytical insights.
Independently manage priorities, risks, dependencies, and technical decisions.
Bachelor's degree in Data Analytics, Data Science, Data Engineering, Computer Science, Information Systems, or a related field; equivalent practical experience is also acceptable.
4+ years of professional experience delivering production analytics or data engineering solutions.
Advanced SQL skills, including complex joins, aggregations, window functions, and query optimization.
Strong hands-on experience with Power BI, including DAX, semantic models, dashboard development, and Power BI Service.
Experience with cloud data warehouse platforms such as Snowflake, BigQuery, Redshift, or Databricks.
Strong understanding of dimensional data modeling and star schema architecture.
Experience building and maintaining ETL/ELT pipelines, incremental loads, and failure-handling processes.
Proficiency in Python or R for data extraction, transformation, and analysis.
Experience translating business requirements into structured analytics solutions.
Strong technical documentation and stakeholder communication skills.
Hands-on experience with Snowflake.
Experience with Power BI Service administration, workspaces, gateways, deployment pipelines, and Row-Level Security.
Familiarity with dbt, Airflow, or Azure Data Factory.
Experience with APIs and system integrations.
Exposure to HR or Talent Acquisition platforms, particularly SmartRecruiters or Workday.
Experience with Workday Prism Analytics or Workday Data Cloud.
Experience working with large datasets across Azure, AWS, or Google Cloud Platform.
Proficiency in Git and analytics engineering best practices.
Experience delivering analytics solutions in consulting or contract environments.
Familiarity with AI/ML techniques and workforce analytics.
Cloud Data Warehousing: Snowflake, BigQuery, Redshift, Databricks
SQL: Advanced SQL, Query Optimization
BI & Visualization: Power BI, DAX, Semantic Models, Power BI Service
Data Modeling: Dimensional Modeling, Star Schema, SCD
Data Engineering: ETL/ELT Pipelines
Programming: Python / R
Integration: REST APIs, System Integrations
Data Governance: Data Quality, Monitoring
Version Control: Git
Production-ready cloud data pipelines.
Deployed dimensional data model.
Complete Power BI dashboard suite.
Governed semantic model with documented measures.
Automated refresh and data quality monitoring.
Technical documentation and troubleshooting runbook.
End-user enablement and knowledge transfer.
Documented operational handover and future analytics roadmap.
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