Position Summary... What you'll do... Come join our People Data Engineering Team @Walmart where you'll have the opportunity to manage people data at Walmart scale and make sure the consistent data is being utilized across transactional and analytical systems. About Team: The Enterprise People Technology team supports the successful deployment and adoption of new People technology across the enterprise. As a Fortune #1 company, our work impacts millions of associates globally. We strive to continuously improve people technology and products to help managers and associates so they can focus on what matters most - supporting our customers and members. People Technology is one of the major segments of Walmart Global Techs Enterprise Business Services, which is invested in building a compact, robust organization that includes service operations and technology solutions for Finance, People, and the Associate Digital Experience. What you'll do:
- Architect, design, and implement high-performance data pipelines and ETL processes for people data.
- Develop and maintain data infrastructure and platforms ensuring scalability, reliability, and security.
- Work closely with stakeholders to understand business requirements and translate them into technical solutions.
- Champion data quality, governance, and best practices across the organization.
- Mentor and guide junior data engineers, fostering a culture of technical excellence.
- Stay abreast of industry trends and emerging technologies, identifying opportunities to enhance our data and analytical capabilities.
- Create Vision statement for large Data lake and design the blue print to modernize data strategy
- Contribute to the development and evolution of our data strategy and roadmap.
- Interact with Walmart engineering teams across geographies to leverage expertise and contribute to the technical community.
- Bachelors/masters degree in computer science or a related field
- With 10+ years experience in data engineering, with a proven track record of building and scaling data-intensive applications.
- Experience with data governance and data privacy best practices
- Expertise in big data technologies like Hadoop, Apache Spark (Scala preferred), Python, Apache Hive, or similar frameworks on the cloud (GCP preferred, AWS, Azure etc.) to build batch data pipelines with strong focus on optimization, SLA adherence and fault tolerance.
- Expertise in building idempotent workflows using orchestrators like Airflow.
- Expertise in writing SQL to analyze, optimize, profile data preferably in BigQuery or SPARK SQL
- Strong data modeling skills are necessary for designing a schema that can accommodate the evolution of data sources and facilitate seamless data joins across various datasets.