Senior Analytics Engineer, People Data

Anduril Industries is seeking a Senior Analytics Engineer focused on People Data to design and maintain the data infrastructure, models, and pipelines that power analytics across HR systems. This onsite role in Costa Mesa, CA centers on turning disparate people data into reliable, accessible insights to support data-driven decisions within the People organization. The position offers a salary range of USD 166,000 to 220,000 per year and requires a strong background in data engineering to scale our analytics capabilities.

Responsibilities

• Design, build, and optimize 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.
• Develop, maintain, and govern scalable and secure data models, schemas, and ontologies specifically for people analytics, ensuring data quality, consistency, and accessibility for downstream consumption.
• Contribute 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).
• Partner 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.
• Implement and monitor data quality checks, identify discrepancies, troubleshoot data issues, and ensure the reliability and integrity of people data across all systems.
• Continuously monitor the performance of data pipelines and models, identifying bottlenecks and implementing solutions to ensure the efficiency and scalability of our people data infrastructure.
• Create and maintain comprehensive documentation for data pipelines, models, and processes, and champion data engineering best practices (e.g., version control, testing, CI/CD) within the team.
• Implement and enforce strict data security measures and ensure all data handling practices comply with internal policies and external regulations (e.g., GDPR, CCPA) related to employee data privacy.
• Collaborate 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.

Requirements

• 5+ years of progressive experience in Data Engineering, Analytics Engineering, or a similar role focused on building and optimizing 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 modeling, Kimball methodology) for analytical purposes.
• Hands-on experience building and optimizing 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.
• Strong technical foundation in data engineering, with expertise in SQL, Python, and experience working with cloud-based data platforms (AWS, GCP, Azure).

Technologies

• SQL, Python
• Snowflake
• Google BigQuery
• AWS Redshift
• Databricks/Delta Lake
• AWS S3
• Azure Data Lake Storage
• dbt
• Apache Airflow
• Flyte
• Dagster
• Iceberg
• SQLMesh
• Palantir Foundry
• Terraform
• CloudFormation
• Docker
• Kubernetes
• Tableau
• Power BI
• Looker
• Rippling
• Workday
• Oracle HCM Cloud
• Apache Spark
• Flink

Benefits

• Equity grants included in the majority of full-time offers and are part of total compensation
• Comprehensive benefits package for full-time employees, including health and recovery support

Preferred Qualifications

• Experience with big data processing frameworks (e.g., Apache Spark, Flink) and advanced data warehousing features like schema evolution or time travel (e.g., 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 Terraform, CloudFormation, Docker, or Kubernetes for managing data infrastructure and deploying applications.
• Familiarity with integrating data pipelines with leading BI tools (Tableau, Power BI, Looker) to optimize 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.