Data Engineer I (SPS)

Candidates usually face three rounds: a technical screen covering SQL, data modeling, and ETL; a second technical interview focusing on AWS, big‑data tools, and scripting; and a final HR round. The difficulty is moderate to high, with emphasis on problem‑solving, system design, and clear communication. Preparation should include hands‑on projects, mock interviews, and revisiting core data engineering concepts.

Round 1: Technical – SQL, data modeling, ETL → Round 2: Technical – AWS, big data, scripting → Round 3: HR

Amazon fosters a culture of customer obsession, ownership, and continuous learning. Employees are encouraged to innovate, experiment, and collaborate across teams. While the work pace is fast, the company offers resources for professional growth and a focus on high impact projects.

Bachelor’s degree or above in Computer Science, Computer Engineering, Information Management, Information Systems, or a related discipline. Minimum 1+ years of data engineering experience. Minimum 60% or 6.5 CGPA. No backlogs. Batch year not specified.

Amazon is a global e‑commerce and cloud computing giant that thrives on data-driven decision making. With a culture that prizes customer obsession, innovation, and operational excellence, Amazon offers engineers the chance to work on projects that touch millions of users worldwide. The Selling Partner Services (SPS) organization focuses on supporting Amazon’s marketplace sellers, and its Selling Partner Insights and Analytics (SPIA) team builds the data platform behind Paragon – Amazon’s second‑largest Human‑in‑the‑Loop system that processes over 500 million cases annually. The Data Engineer I (SPS) role is a builder’s position that blends data architecture, platform engineering, and analytics enablement. You will design and operate scalable, cost‑effective data pipelines on native AWS technologies, curate data for reporting, analytics, and large language model (LLM) training, and partner with business owners to translate requirements into robust data solutions. Key Responsibilities (8‑10 points): 1. Design and maintain scalable data infrastructure on AWS (S3, EMR, Redshift, Athena). 2. Build and optimize ETL pipelines using Python, Spark, Hive, and SQL. 3. Define logical data models and star/snowflake schemas that support Paragon’s growth. 4. Implement data quality, lineage, and governance controls. 5. Automate monitoring, alerting, and cost‑optimization tasks. 6. Collaborate with cross‑functional teams to gather requirements and deliver data solutions. 7. Drive Best‑At‑Amazon (BAA) standards for performance, reliability, and compliance. 8. Enable data exploration for large datasets and enforce access controls. 9. Participate in code reviews, unit testing, and documentation. 10. Mentor junior engineers and share best practices. Tech Stack: SQL, Python, KornShell, Hadoop, Hive, Spark, EMR, Informatica/ODI/SSIS, AWS S3, IAM, Redshift, Athena, LLM/ML data pipelines. Growth Path: Entry as Data Engineer I → Data Engineer II → Senior Data Engineer → Lead Data Engineer or Data Architecture roles. Opportunities also exist to transition into Analytics Engineering, ML Engineering, or Engineering Management, leveraging the AWS and big‑data expertise gained. Why Join Amazon? The company offers a high‑impact environment where your work directly influences customer experience and operational excellence. You’ll benefit from world‑class mentorship, continuous learning, and the chance to work with cutting‑edge technologies at scale. The culture rewards ownership, experimentation, and rapid iteration, making it an ideal place for ambitious engineers. With a reputation for competitive compensation, diverse projects, and a global footprint, Amazon remains a top destination for data engineers seeking challenging, high‑visibility roles.