Data Engineer II, Fire TV
Amazon · Not specified · onsite · Posted 56d ago
About a minute. You'll make an account on the way through.
About this role
Description
Fire TV, Advertising and Appstore is reshaping the way millions of people discover, engage with, and enjoy entertainment every day. The FAA Decision Science organization is looking for a Data Engineer to help build scalable, reliable, and compliant data systems that support millions of customers worldwide.
This role contributes to the foundational data infrastructure used by Data Science, Business Intelligence, Product, Finance, Advertising, Engineering, and leadership teams to make high-quality business decisions. You will design, build, and operate data pipelines, data models, data quality mechanisms, and cloud-based infrastructure supporting Fire TV engagement, lifecycle analytics, Ads monetization, Appstore performance, executive reporting, forecasting, experimentation, and AI-enabled analytics.
This role is appropriate for an engineer who can independently own defined data systems or workstreams while partnering with senior engineers and cross-functional stakeholders on broader technical direction. You will be expected to work through moderate ambiguity, clarify requirements, make sound implementation trade-offs, improve operational reliability, and deliver scalable solutions that reduce manual effort over time. Successful engineers in this role improve reliability, automate recurring work, simplify data systems, document operational processes, and build trusted data products that help the business move faster.
Key job responsibilities
Own the design, development, testing, deployment, and operation of data pipelines and datasets within an assigned domain
Build and maintain scalable ETL/ELT workflows using SQL, Python, AWS services, and big data technologies
Operate and improve data infrastructure, including Redshift clusters, data lake tables, orchestration workflows, monitoring, alerting, and data quality controls
Improve operational reliability by identifying recurring failures, reducing manual intervention, automating recovery steps, and creating clear runbooks
Partner with Data Science, Business Intelligence, Product, Finance, Engineering, Privacy, and Legal stakeholders to translate business and compliance requirements into scalable data solutions
Build and operate conversational, self-service, and agentic analytics data products
Support compliance, privacy, retention, and governance initiatives, including GDPR, DMA, telemetry migration, data access controls, and retention workflows
Contribute to data foundations that support forecasting, experimentation, ML/AI use cases, self-service analytics, and certified business metrics
Implement data validation, lineage, documentation, and operational mechanisms that improve trust and reduce single points of failure
Drive scoped modernization efforts such as pipeline simplification, migration support, Redshift/data lake improvements, automation, and self-service data enablement
Clarify ambiguous requirements, identify data quality or source-of-truth gaps, and escalate broader trade-offs to senior engineers or managers when appropriate
Mentor junior engineers on scoped technical tasks, coding standards, operational practices, and data quality expectations
Participate in on-call and product support for business-critical pipelines and datasets
Own the design and operation of the data foundations that power GenAI, RAG, and agentic analytics within an assigned domain
Build guardrails, validation, and evaluation mechanisms, both automated and human-in-the-loop, that keep AI-generated outputs such as SQL and metrics accurate and reliable
Apply AI coding assistants and agentic development tools to your daily work and share effective patterns with the team to raise overall engineering velocity.
Basic Qualifications
- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with database, data warehouse or data lake solutions
- Experience with one or more scripting language (e.g., Python, KornShell, Scala)
Preferred Qualifications
- 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 implementing scalable compliance solutions (technology, procedures, processes, etc.) in a regulated environment
- Experience with big data technologies such as AWS, Hadoop, Spark, Pig, Hive etc.
- Experience leveraging AI to build customer facing solutions and/or improving internal productivity
- Experience handling AI/ML infrastructure
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