People Data Analytics Engineer

This role is central to our People Data & Analytics team, where you will be instrumental in building and maintaining the robust data infrastructure that powers our strategic insights.

You'll own the full data lifecycle, from ensuring accurate ingestion and integration of diverse HR data sources, to designing, developing, and optimising data models and pipelines.

Your primary objective will be to transform raw, disparate information into clean, reliable, and analytics-ready datasets, empowering our People Analysts and business stakeholders to unlock deeper understanding of our workforce, enhance employee experience, and drive data-driven decision-making.

Success in this role requires a strong technical foundation, meticulous attention to data quality, and a passion for crafting efficient data solutions.

Key responsibilities include:

• Designing, building, and optimising robust ETL/ELT pipelines to reliably ingest, integrate, and transform diverse people data from various HR systems (HRIS, ATS, LMS, etc.) into our data platform.

• Developing, maintaining, and governing scalable and secure data models, schemas, and ontologies specifically for people analytics, ensuring data quality, consistency, and accessibility for downstream consumption.

• Contributing to the strategic design, development, and evolution of our people data platform and tooling, advocating for engineering best practices, automation, and a scalable analytics ecosystem (e.g., leveraging SQLMesh, Iceberg, Flyte).

• Partnering closely with People Analysts, HR Business Partners, and other stakeholders to understand their analytical needs and translate them into robust data solutions, providing well-structured, documented, and reliable datasets.

• Implementing and monitoring data quality checks, identifying discrepancies, troubleshooting data issues, and ensuring the reliability and integrity of people data across all systems.

• Continuously monitoring the performance of data pipelines and models, identifying bottlenecks and implementing solutions to ensure the efficiency and scalability of our people data infrastructure.

• Creating and maintaining comprehensive documentation for data pipelines, models, and processes, and championing data engineering best practices (e.g., version control, testing, CI/CD) within the team.

• Implementing and enforcing strict data security measures and ensuring all data handling practices comply with internal policies and external regulations (e.g., GDPR, CCPA) related to employee data privacy.

• Collaborating with broader enterprise analytics and data engineering teams to align on data architecture standards, integrate people data with other business domains, and contribute to the overall evolution of the company's data platform.

Required qualifications include:

• 5-7 years of progressive experience in Data Engineering, Analytics Engineering, or a similar role focused on building and optimising data pipelines and data infrastructure.

• Expert-level proficiency in SQL for complex data manipulation and querying, and advanced Python for scripting, data processing, and automation.

• Extensive experience with cloud-based data warehousing solutions (e.g., Snowflake, Google BigQuery, AWS Redshift, Databricks/Delta Lake) and data lake technologies (e.g., AWS S3, Azure Data Lake Storage).

• Deep understanding and proven experience in designing, implementing, and maintaining robust data models (e.g., dimensional modelling, Kimball methodology) for analytical purposes.

• Hands-on experience building and optimising complex ETL/ELT processes and data pipelines using modern tools such as dbt, Apache Airflow, Flyte, Dagster, or similar orchestration platforms.

• Excellent communication skills, both written and verbal, with the ability to translate technical concepts for non-technical stakeholders and collaborate effectively across diverse teams.

• Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field.

Preferred qualifications include:

• Experience with big data processing frameworks (e.g., Apache Spark, Flink) and advanced data warehousing features like schema evolution or time travel (e.g., Apache Iceberg, Delta Lake).

• Direct hands-on experience with tools mentioned in our stack like Palantir Foundry, SQLMesh, Flyte, or similar cutting-edge data orchestration and transformation platforms.

• Relevant professional certifications from major cloud providers (e.g., AWS Certified Data Analytics - Specialty, Google Cloud Professional Data Engineer).

• Experience with tools like Terraform, CloudFormation, Docker, or Kubernetes for managing data infrastructure and deploying applications.

• Familiarity with integrating data pipelines with leading BI tools (e.g., Tableau, Power BI, Looker) to optimise dashboard performance and data accessibility for end-users.

• Deep expertise working with data from specific enterprise HRIS systems like Rippling, Workday, and Oracle HCM Cloud including their data models and APIs.

• Solid understanding of HR data concepts, metrics, and common HR systems (HRIS, ATS, LMS), with a strong interest in People Analytics.