Sr Snowflake Data Engineer

Job Description <\/b><\/span><\/span><\/span><\/u>
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We are seeking a Senior Data Engineer to design, build, and optimize scalable data solutions that power critical business operations and client initiatives. This role requires deep expertise in SQL, Python, and enterprise\-scale ETL development, with strong experience supporting cloud\-based data platforms and migration efforts.
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You’ll collaborate with cross\-functional teams to develop high\-performing data pipelines, ensure data reliability, and contribute to engineering best practices across the data ecosystem.
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Skill Set: <\/b><\/span><\/span><\/span><\/u>
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• +7 years in Python, Pyspark, and Snowflake. Proven expertise in data modeling, performance tuning, optimization, AWS, and Cortex AI.
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• Strong experience with ETL tools and processes.
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• Working knowledge of React.JS.
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  Responsibilities <\/b><\/span><\/span><\/span><\/u>
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  • Design, build, and maintain scalable <\/span><\/span> data pipelines <\/span><\/span><\/b> using Python, PySpark, and Snowflake.
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  • Develop and optimize <\/span><\/span> Snowflake data models <\/span><\/span><\/b> , including schema design, clustering, micro\-partitioning strategies, and query performance tuning.
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  • Implement <\/span><\/span> AI\-enabled data workflows <\/span><\/span><\/b> using Snowflake Cortex AI or similar AI\/ML\-integrated data platforms.
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  • Build robust backend data services and APIs supporting enterprise analytics and application data needs.
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  • Lead end\-to\-end implementation of data engineering solutions across cloud environments, especially AWS.
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  • Develop reusable ELT frameworks and support data ingestion from diverse structured and unstructured sources.
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  • Apply strong SQL expertise to create efficient, scalable data transformations and data marts.
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  • Support or collaborate with frontend teams using React.js in data\-driven product development.
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  • Work with distributed data processing tools and modern compute frameworks.
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  • Ensure data quality, data governance, and compliance with enterprise security policies.
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  • Partner with data science, analytics, architecture, and product teams to deliver scalable, high\-impact data products.
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  • Troubleshoot complex data issues, optimize compute costs, and drive continuous improvement.
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    Requirements <\/h3>

    Required Qualifications: <\/b><\/span><\/span><\/span><\/u>
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    • 7+ years <\/span><\/span><\/b> of experience in data engineering or backend software development. <\/span>
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    • Strong hands\-on experience with <\/span><\/span> Python <\/span><\/span><\/b> , <\/span><\/span> PySpark <\/span><\/span><\/b> , and <\/span><\/span> Snowflake <\/span><\/span><\/b> . <\/span>
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    • Proven expertise in <\/span><\/span> Snowflake performance tuning <\/span><\/span><\/b> , query optimization, and warehousing best practices. <\/span>
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    • Experience with <\/span><\/span> Snowflake Cortex AI <\/span><\/span><\/b> or other AI\-enhanced data platform capabilities. <\/span>
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    • Deep understanding of <\/span><\/span> modern data warehousing <\/span><\/span><\/b> concepts including dimensional modeling, ELT\/ETL patterns, and data mart design. <\/span>
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    • Advanced SQL skills and experience designing scalable, production\-grade data models. <\/span>
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    • Experience delivering <\/b><\/span><\/span> end\-to\-end data engineering solutions <\/span><\/span><\/b> in cloud environments (AWS preferred). <\/b><\/span>
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    • Working knowledge of <\/span><\/span> React.js <\/span><\/span><\/b> for supporting or developing data\-driven UI components. <\/span>
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    • Strong understanding of <\/span><\/span> distributed data processing frameworks <\/span><\/span><\/b> (e.g., Spark, Snowpark, Kafka). <\/span>
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    • Solid understanding of data governance, security, and privacy best practices. <\/span>
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      Preferred Qualifications: <\/b><\/span><\/span><\/span><\/u>
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      • Experience with Snowpark, Streamlit\-in\-Snowflake, or Snowflake Native Apps. <\/span>
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      • Familiarity with CI\/CD for data engineering workloads. <\/span>
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      • Knowledge of containerization technologies such as Docker or Kubernetes. <\/span>
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      • Experience supporting AI\/ML workloads in production environments. <\/span>
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        <\/body>
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