Data Engineer, WWGS GRIT [Worldwide Grocery Stores Data Engineer]

Amazon Dev Center India (ADCI) is hiring a technically sharp, process-oriented Data Engineer, WWGS GRIT to join our Worldwide Grocery Stores tech tower in Bengaluru, Karnataka . Operating at the core of Amazon's massive physical and digital grocery portfolios—including Amazon Fresh and Whole Foods Market—this engineering role centers on architecting, scaling, and maintaining the global data infrastructure that guides pricing, selection, promotions, and customer discovery models .

Key Responsibilities & Scope of Work

• ETL/ELT Pipeline Engineering: Design, develop, and support resilient, low-latency pipelines to ingest and process large-scale datasets from disparate sources into cloud data warehouses and data lakes .
• Advanced Data Modeling: Build and optimize multi-dimensional data models tailored to support predictive analytics, business intelligence dashboards, and downstream machine learning use cases .
• Pipeline Telemetry & Triage: Proactively monitor pipeline health and execute rapid real-time troubleshooting to resolve processing exceptions within established Service Level Agreements (SLAs) .
• Quality Governance & Auditing: Implement automated data validation rules, integrity checks, and real-time alerting systems to eliminate data variance patterns .
• Cross-Functional Tech Interlock: Partner closely with Business Intelligence Engineers, Data Scientists, and grocery business managers to map out functional parameters and deliver fit-for-purpose solutions .
• SDLC & Engineering Best Practices: Author comprehensive architectural documentation, lead internal code reviews, and build Python/KornShell automation scripts to retire manual touchpoints .

Required Qualifications & Technical Stack

• Data Domain Experience: Minimum of 1 year of verified, non-internship professional history operating within data engineering, infrastructure development, or warehousing specialties .
• Query Language Fluency: Practical programming mastery utilizing at least one major query tool such as SQL, SparkSQL, PL/SQL, HiveQL, or Scala .
• Scripting Tool Stack: Strong functional competency drafting automation or data transformation layers using Python or KornShell scripting .
• Preferred Technical Value-Adds: Hands-on infrastructure exposure leveraging AWS cloud environments and big data tools (Hadoop, Hive, Spark, EMR) or traditional enterprise ETL platforms (Informatica, SSIS, Datastage) .
• Core Soft Skills: Outstanding verbal and written business communication attributes, high-level logical troubleshooting, and an unyielding commitment to data reliability .