Description
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.
Requirements
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.
Technology Doesn't Change the World, People Do.