Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies—all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows, from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels, which they can cancel at any time, and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced growth business, available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on.
The Prime Video Content Analytics and Products team is looking for an experienced Data Engineer. As a Data Engineer, you will design, build, and operate scalable data pipelines and data models that power Prime Video’s customer-facing features and internal analytics. You’ll solve complex data warehousing and big-data processing challenges using AWS technologies, delivering self-service analytics, infrastructure-as-code, and high-performance ETL/ELT workflows. You will also develop automated data quality frameworks that validate accuracy, detect anomalies, and increase trust in downstream data products. In this role, you will partner closely with business, science, and engineering teams to tackle non-standard data problems and deliver high-impact solutions that scale with rapid growth and evolving business needs.
Key job responsibilities
- Build and optimize data pipelines to ingest and transform data from various sources, including traditional ETL pipelines and event data streams.
- Utilize data from disparate sources to build meaningful datasets for analytics and reporting, focusing on consolidating data from various Prime Video systems.
- Implement big-data technologies (e.g., Redshift, EMR, Spark, SNS, SQS, Kinesis) to optimize processing of large datasets.
- Develop and maintain the team's data platform, including infrastructure-as-code using AWS CDK.
- Work closely with business stakeholders to understand their needs and translate them into technical solutions.
- Analyze business processes, logical data models, and relational database implementations.
- Write high-performing SQL queries.
- Design and implement automated data processing solutions and data quality controls.
- Collaborate with software engineers to support the data needs of products
- Participate in on-call rotations to support the team's products and data pipelines.
- Optimize data processing and storage solutions to improve performance and reduce costs.
About the team
The Prime Video Content Analytics & Products team is dedicated to developing software and business intelligence products that streamline the process of planning, configuring, and tracking content launches at every stage of the title lifecycle, from the initial concept through production to post-launch analysis.- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
- Experience working on and delivering end to end projects independently
- Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience as a data engineer or related specialty (e.g., software engineer, business intelligence engineer, data scientist) with a track record of manipulating, processing, and extracting value from large datasets
- Experience with SQL- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
- Experience with Apache Spark / Elastic Map Reduce
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