Job Summary
The Corporate Data team enables data-informed decision-making across the organization’s People & Workplace functions. The team delivers critical data infrastructure and insights that help leaders better attract, develop, engage, and retain talent across the entire employee lifecycle.
As a Data Engineer, you will play a key role in scaling and maintaining the corporate people data warehouse, supporting analytics and reporting used by teams across the organization. You will design and maintain data pipelines, ensure data reliability at scale, and help power insights related to workforce analytics, recruiting, employee engagement, organizational health, and program effectiveness.
This role is ideal for engineers who enjoy working with large-scale data systems, distributed processing frameworks, and high-volume ETL pipelines while collaborating closely with analysts and stakeholders
Responsibilities
Design, build, and maintain scalable data infrastructure to support analytics and reporting across the organization.
Develop and operate ETL pipelines to ingest, transform, and deliver large-scale datasets.
Work with distributed data processing frameworks such as Spark, Hive, or similar MPP architectures.
Use SQL and data modeling techniques to structure and optimize datasets for analytics use cases.
Process and analyze large volumes of structured and semi-structured data using tools such as Spark and Presto.
Write production-quality code using Python, Java, Scala, or Go.
Ensure data reliability and availability, operating and monitoring hundreds of ETL pipelines with strict SLAs.
Investigate and resolve complex data issues, including root-cause analysis of pipeline failures or data inconsistencies.
Partner closely with Data Analysts and cross-functional stakeholders to provide reliable datasets and guide them in using data effectively.
Troubleshoot data issues in dashboarding tools (e.g., Tableau, Power BI, MicroStrategy) and propose solutions.
Qualifications and Job Requirements
5+ years of experience in Data Engineering, building and maintaining data infrastructure and pipelines.
Strong expertise in SQL, including joins, aggregations, unions, and window functions.
Hands-on experience with data modeling and schema design for analytical systems.
Experience building ETL pipelines using Airflow or similar orchestration tools.
Experience with Big Data ecosystems, including Hadoop, Hive, Spark, or related technologies.
Programming experience in Python, Java, Scala, or Go.
Familiarity with UNIX/Linux environments and shell scripting.
Understanding of software engineering best practices, including testing, monitoring, and documentation.
Strong collaboration and communication skills when working with analysts and cross-functional stakeholders.
Ability to troubleshoot and resolve data issues across pipelines and BI tools.
Nice to Have
Degree in Computer Science or a related field.
Experience working in fast-paced, high-growth technology companies.
Familiarity with real-time data ingestion frameworks such as Kafka or Flume.
Experience supporting data science or advanced analytics teams.
Knowledge of industry best practices for large-scale ETL and data platform architecture.
Strong interest in data science and emerging data technologies.