Talent Acquisition Data Engineering & Analytics Specialist

The hiring client is confidential. The Talent Acquisition Data Engineering & Analytics Specialist will build a production reporting foundation, from recruiting data pipelines and dimensional modeling to automated Power BI dashboards, documentation, and stakeholder enablement.

Responsibilities:

• Establish and validate connections between Talent Acquisition source systems (SmartRecruiters, Workday, Jira, ServiceNow) and the cloud data warehouse, partnering with Data Engineering and IT
• Build and maintain production-grade ELT pipelines, including API-based ingestion where source systems require it
• Configure Power BI connectivity and scheduled refresh so dashboards update automatically without manual data pulls
• Implement data quality checks and refresh monitoring so reporting issues surface and are remediated before leadership sees them
• Design, build, and deploy a query-ready dimensional data model supporting requisition, funnel, and hiring activity analytics
• Write and optimize SQL transformations that turn raw source system data into analysis-ready tables, including historical and point-in-time reporting such as requisition status history and pipeline snapshots
• Define table grain, conformed dimensions, and slowly changing dimension patterns appropriate to recruiting workflows
• Develop a full Power BI dashboard suite covering the Talent Acquisition metrics framework, including executive summary, requisition and pipeline health, funnel conversion, time-to-fill and time-in-stage, offer metrics, sourcing effectiveness, recruiter workload, and TA operational SLA metrics
• Build a governed semantic model with certified DAX measures so metrics are defined once and consumed consistently across all reporting
• Implement row-level security and optimize model performance at full data volume
• Produce implementation-ready documentation covering pipeline architecture, data lineage, model diagrams, measure definitions, and a refresh and troubleshooting runbook
• Establish metric definitions and secure stakeholder agreement on contested or ambiguous definitions before publication
• Create end-user guides and deliver enablement sessions for TA Operations and HR leadership
• Deliver a documented handover so Client teams can operate and extend the solution after the engagement ends
• Partner with Talent Acquisition leadership to prioritize requirements, translate business questions into measurable metrics, and present dashboard walkthroughs and insights
• Operate independently with limited day-to-day oversight, proactively surfacing risks, dependencies, and trade-offs

Requirements:

• Bachelor's degree in Data Analytics, Data Science, Data Engineering, Computer Science, Information Systems, or a related field; or equivalent practical experience
• 4+ years of professional experience delivering production analytics or data engineering solutions
• Advanced SQL, including complex joins, aggregations, window functions, and query optimization against large datasets
• Demonstrated Power BI delivery experience, including authoring DAX measures, designing semantic models, and publishing to Power BI Service
• Hands-on experience with a cloud data warehouse platform (e.g., BigQuery, Redshift, Databricks, Snowflake, or comparable)
• Proven experience designing and implementing dimensional data models in a production environment
• Experience building and maintaining ETL/ELT pipelines, including incremental loads and failure handling
• Proficiency in Python (or R) for data extraction, transformation, and analysis
• Demonstrated ability to translate ambiguous business questions into structured, measurable analytics requirements and to drive stakeholder agreement on metric definitions
• Experience documenting technical solutions and enabling non-technical end users
• Cloud data warehouse platforms (BigQuery, Redshift, Databricks, Snowflake)
• SQL (advanced) and query optimization
• Power BI (authoring, DAX, semantic models, Power BI Service)
• Data modeling (dimensional, star schema, slowly changing dimensions)
• ETL/ELT pipeline development
• Python/R data transformation
• APIs and system integrations
• Data quality frameworks and monitoring
• Git and version control
• Experience with Snowflake
• Experience with Power BI Service administration: workspaces, gateways, deployment pipelines, and row-level security
• Experience with orchestration and transformation tooling (dbt, Airflow, Azure Data Factory)
• Experience with APIs and system-to-system data integrations
• Experience with HR or Talent Acquisition source systems, particularly SmartRecruiters or Workday
• Experience with Workday Prism Analytics or Workday Data Cloud
• Experience with large datasets in cloud environments (Azure, AWS, or GCP)
• Proficiency with version control (Git) and analytics engineering practices
• Prior experience delivering analytics solutions in a contract or consulting capacity, including structured handover
• Familiarity with AI/ML techniques and interest in applying them to workforce data
• Interest in HR, Talent Acquisition, or workforce analytics and how data informs business strategy