● 5+ years of experience in Analytics Engineering, Data Engineering, Business Intelligence Engineering, or a similar role, preferably in People Analytics, HR data, or another sensitive data environment.
● Expert-level proficiency with modern cloud data warehouses such as Google BigQuery, Snowflake, or similar platforms, with advanced SQL and strong data modeling skills.
● Hands-on experience with dbt, git workflows, automated testing, and data orchestration tools such as Airflow, Prefect, or Dagster.
● Strong understanding of data security principles, including encryption at rest and in transit, cloud KMS, IAM, service accounts, secrets management, key rotation, and secure data-sharing patterns.
● Experience working with PII or sensitive employee data, including practical knowledge of masking, tokenization, pseudonymization, row-level security, column-level security, or controlled aggregation.
● Working knowledge of CI/CD practices, deployment workflows, environment management, and containers such as Docker.
● Exposure to AI/ML, LLM, or chatbot-related data use cases, with Python proficiency highly preferred.
● Experience building dashboards, reports, and self-service analytics products for business users, preferably using Tableau or similar BI tools.
● Deep understanding of HR functions and how data structures, systems, definitions, and processes differ across domains such as workforce, recruiting, compensation, benefits, performance, employee relations, learning, and talent management.
● Experience working with contemporary HR technology platforms is preferred, such as Workday, ServiceNow, or similar enterprise systems.
● Ability to plan and execute complex projects, prioritize competing demands across individual and team responsibilities, and communicate effectively with cross-functional business and technical stakeholders.
● Strong systems thinking and the ability to influence stakeholders while maintaining high standards for data quality, security, documentation, and governance.