Data Engineer

Veeva Systems is a mission-driven organization and pioneer in industry cloud, helping life sciences companies bring therapies to patients faster. The Data Engineer role involves owning the end-to-end development lifecycle, collaborating with a high-performing engineering team to design, build, and deploy high-impact features for Veeva’s life sciences customers.

Responsibilities:

• Architect and build resilient, distributed data processing systems using Python and Spark on AWS
• Design and implement end-to-end ETL/ELT workflows that ingest and unify data from diverse sources —ranging from modern table formats like Iceberg and Delta to legacy business files such as Excel and CSV —ensuring a scalable and consistent single source of truth for the organization
• Lead the implementation of the Medallion Architecture, managing data maturity through Bronze, Silver, and Gold layers. You will define how data is structured, classified, and stored to maximize business value while ensuring scalability and high availability
• Build reusable libraries and frameworks for data quality validation, metadata tracking, and pipeline monitoring
• Build CI/CD process, to automate deployment and testing to maintain a high bar for engineering excellence
• Enforce data governance standards, including security, privacy, and regulatory compliance
• Proactively monitor system health, implement automated observability, and resolve complex bottlenecks in distributed systems to ensure peak resource efficiency and cost-effectiveness
• Partner directly with Product Managers and Data Scientists to translate business requirements into innovative solutions
• Own the full feature lifecycle—from initial whiteboarding to production deployment and long-term maintenance

Requirements:

• 4+ years of professional data engineering experience with a demonstrated ability to architect and deploy production-grade data platforms from scratch
• Expert-level proficiency in Python and Apache Spark, with specific experience in JVM tuning, memory management, and optimizing execution plans for large-scale distributed workloads
• Deep expertise in modern data architecture, software design patterns, and various data modeling techniques designed for scalability and performance
• Proven track record of building on AWS (primary) or GCP, including hands-on experience with managed services like EMR or Databricks
• Extensive experience designing and managing complex data lifecycles using orchestration tools such as Airflow, AWS Step Functions, or Prefect
• Deep understanding of data cleansing, curation, and transformation strategies, coupled with experience implementing data governance, security, and lifecycle management policies
• Strong background in building reusable libraries, frameworks, and internal tools that standardize data ingestion and automate ETL/ELT workflows
• Exceptional debugging skills for distributed systems and resolving performance bottlenecks at scale
• Proficiency with CI/CD tools and processes (e.g. Codefresh, Jenkins)
• Excellent verbal and written communication skills in English, with the ability to translate complex technical architectures into actionable insights for stakeholders and cross-functional teams
• Must be located in EST or CST
• Applicants must have the unrestricted right to work in the United States. Veeva will not provide sponsorship at this time
• Relevant certifications (e.g., AWS, Spark, or similar)
• Familiarity with streaming and distributed technologies such as Spark Streaming, EKS, Kinesis, or Apache Kafka
• Experience implementing or managing modern cloud data warehouses or lakehouse architectures
• Prior experience working in the Life Sciences industry