Mkt Tech BI team owns one of the largest datasets at Amazon, our team has various backgrounds that provide you the opportunity to learn each other. Our ultimate goal is to build a robust/scalable data infrastructure and automated reporting system to empower users have maximum flexibility to play with the data in order to maximize Long Term Free Cash Flow(LTFCF).
The ideal candidate relishes working with large volumes of data, enjoys the challenge of highly complex technical contexts, and, above all else, is passionate about data and analytics. They are an expert with data modeling, ETL design and business intelligence tools and passionately partners with the business to identify strategic opportunities where improvements in data infrastructure creates out-sized business impact. He/she is a self-starter, comfortable with ambiguity, able to think big (while paying careful attention to detail), and enjoys working in a fast-paced and global team. It's a big ask, and we're excited to talk to those up to the challenge!
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
- 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.