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Strong global brand, metro location, and mid-level analytics engineer role with broad skill requirements.
High because HR data sensitivity and required HR systems domain knowledge reduce cross-industry transferability.
High due to explicit 5+ years requirement and mandatory dbt, cloud warehouse, and PII security expertise.
Design, scale, and optimize the core HR data warehouse architecture to support enterprise-wide workforce analytics, AI, and machine learning workloads.
Partner with Data Science, AI, and HR technology teams to prepare data models and governed pipelines for predictive models, LLM applications, and chatbot analytics.
Build and support secure, privacy-aware analytics solutions, dashboards, and self-service products for HR and business stakeholders, ensuring data governance and protection of sensitive employee data.
5+ years of experience in Analytics Engineering, Data Engineering, or Business Intelligence Engineering, preferably with People Analytics or sensitive data.
Expert proficiency with cloud data warehouses (Google BigQuery, Snowflake), advanced SQL, data modeling, and tools like dbt, git, and data orchestration (Airflow/Prefect/Dagster).
Strong knowledge of data security principles including encryption, IAM, masking, tokenization, row/column-level security to protect PII and sensitive employee data.
Experience building dashboards and analytics products using Tableau or similar BI tools; working knowledge of CI/CD and containerization (Docker).
Technically strong engineer with a background bridging data engineering and AI/ML enablement, including hands-on exposure to Python and AI/ML data workflows.
Experience working within global HR data domains and familiarity with HR tech platforms such as Workday or ServiceNow.
Able to manage complex projects involving cross-functional stakeholders, prioritizing data quality, governance, and secure development practices.