Duties & Responsibilities
Design, develop, and maintain scalable ETL/ELT data pipelines to support business and analytics needsWrite, tune, and optimize complex SQL queries for data transformation, aggregation, and analysisTranslate business requirements into well-designed, documented, and reusable data solutionsPartner with analysts, data scientists, and stakeholders to deliver accurate, timely, and trusted datasetsAutomate data workflows using orchestration/scheduling tools (Airflow, ADF, Luigi,Databricks etc.)Develop unit tests, integration tests, and validation checks to ensure data accuracy and pipeline reliabilityDocument pipelines, workflows, and design decisions for knowledge sharing and operational continuityApply coding standards, version control practices, and peer code reviews to maintain high-quality deliverablesProactively troubleshoot, optimize, and monitor pipelines for performance, scalability, and cost efficiencySupport function roll outs, including being available for post-production monitoring and issue resolutionRequirementsBasic QualificationsBachelor’s degree in computer science, Information Systems, Engineering, or a related field3–5 years of hands-on experience in data engineering and building data pipelinesAt least 3 years of experience in writing complex SQL queries in a cloud data warehouse/ data lake environment.At least 2 years experience in BI development ( e.g. Power BI preferred,Tableau ) Solid hands-on experience with data warehousing concepts and implementationsAt least 3 year of experience with Snowflake or another modern cloud data warehouseAt least 3 year of hands-on Python/ Pyspark development (scripting, OOP, ETL/ELT automation)Familiarity on Data modeling and Data warehousing conceptsExperience with orchestration tools (e.g., Airflow, ADF, Luigi,Databricks Workflow)Familiarity with at least one cloud platform (AWS, Azure, or GCP)Strong analytical, problem-solving, and communication skillsAbility to work both independently and as part of a collaborative teamPreferred QualificationsExperience with DBT (Data Build Tool) for data transformationsExposure to Databricks stack.Familiarity with CI/CD and version control (Git) in data engineering projectsExposure to the e-commerce or customer data domainUnderstands the technology landscape, up to date on current technology trends and new technology, brings new ideas to the team

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