We are looking for a Data Engineer to join the client’s User Platforms organization. This role will be responsible for building and scaling the data foundation that powers experimentation, causal analysis, product optimization, and decision-making across Disney+, Hulu, ESPN+, and other streaming products.
Key Responsibilities:
· Design, develop, and maintain scalable experimentation data platforms supporting A/B testing, geo experiments, feature launches, and product analytics. · Build and optimize batch and streaming data pipelines that ingest, transform, validate, and serve large-scale user behavior, product, and engagement datasets. · Create reusable experimentation frameworks, datasets, and tooling that enable teams to efficiently define experiments, measure outcomes, and analyze results. · Design dimensional models, analytical data products, and feature-ready datasets optimized for analytics, experimentation, and machine learning use cases. · Implement automated data quality validation, monitoring, lineage tracking, testing, and governance controls to ensure reliable and trustworthy experiment results. · Develop production-ready deployment workflows, CI/CD pipelines, observability solutions, and operational processes for data products and services. · Collaborate with Product Managers, Software Engineers, Data Scientists, and Analytics teams to translate business requirements into scalable technical solutions. · Drive best practices for data architecture, engineering standards, experimentation methodologies, and platform reliability. · Mentor engineers and promote a culture of technical excellence, innovation, experimentation, and continuous improvement.
Core Qualifications:
· Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or a related technical discipline. · 7+ years of experience in Data Engineering, Analytics Engineering, Data Platform Engineering, or related fields. · Strong experience designing, building, and supporting large-scale distributed data pipelines and data platforms. · Deep understanding of data modeling, ETL/ELT architectures, data warehousing, lakehouse technologies, and streaming data systems. · Hands-on experience building data solutions that support experimentation, product analytics, personalization, customer insights, or machine learning initiatives. · Advanced proficiency in SQL and Python. · Experience with modern data engineering technologies such as Databricks, Snowflake, Apache Spark, Kafka, Airflow, or comparable platforms. · Experience implementing automated testing, monitoring, data quality frameworks, observability, and governance processes. · Strong knowledge of cloud-based data ecosystems and distributed computing environments. · Ability to communicate complex technical concepts to both technical and non-technical stakeholders. · Proven ability to lead large-scale technical initiatives and deliver highly reliable production-grade data solutions.
