Senior Data Engineer

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.

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