> Markdown version of [/jobs/ext/3022151-data-engineer-marketing-tech-bi-stores-finance-analytics-insights](https://www.wearedevelopers.com/jobs/ext/3022151-data-engineer-marketing-tech-bi-stores-finance-analytics-insights). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Engineer, Marketing Tech BI, Stores Finance Analytics & Insights

- **Company:** Amazon.com, Inc.
- **Location:** Seattle, WA, United States
- **Salary:** $132,100.0 - $178,800.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Business Analytics Applications, Big Data, Information Systems, Databases, Data Validation, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Stores, Programming Tools, Graph Database, Apache Hadoop, Apache Hive, Identity and Access Management, Python (Programming Language), Korn Shell, Machine Learning, Online Analytical Processing, Scala (Programming Language), SQL Databases, Scripting, Apache Spark, Electronic Medical Records, Data Lakes, Information Technology, AWS Glue, Non-relational Database, Data Pipelines, Amazon Redshift
- **Published:** September 21, 2026
- **Apply:** https://dejobs.org/x/x/9F3F9D87CCED439C96CBCB64343E8C95/job/

## About the Role

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!, * 3+ years of data engineering experience

 * 1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
 * 1+ years of developing and operating large-scale data structures for business intelligence analytics using OLAP technologies experience
 * 1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience
 * Bachelor's degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field
 * Experience with data modeling, warehousing and building ETL pipelines
 * Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
 * Experience with one or more scripting language (e.g., Python, KornShell, Scala), * 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 AI/ML technologies

## Description

* 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.


## Related Videos

- [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology)
- [How we built an AI-powered code reviewer in 80 hours](https://www.wearedevelopers.com/videos/1511-how-we-built-an-ai-powered-code-reviewer-in-80-hours)
- [JavaScript? No. Java Scripts! - Scripting with Java](https://www.wearedevelopers.com/videos/2094-javascript-no-java-scripts-scripting-with-java)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data)
- [Tips, Techniques, and Common Pitfalls Debugging Kafka](https://www.wearedevelopers.com/videos/838-tips-techniques-and-common-pitfalls-debugging-kafka)

## Related Articles

- [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)
- [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)
- [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)