We are seeking a highly skilledSenior Data Engineer with 8–15 years of experience in designing, developing, and implementing enterprise-scale data solutions. The ideal candidate will possess strong expertise inSnowflake and hands‑on experience withPython, PySpark, AWS, and Airflow for building scalable, high-performance data platforms. The role demands strong technical leadership, problem‑solving capabilities, and the ability to collaborate effectively with cross‑functional teams to deliver robust data engineering solutions.
Key Responsibilities
Design, develop, and optimize data pipelines and data modelsusing Snowflakefor enterprise analytics and reporting
Write and optimize complex SQL / PL‑SQL queries, procedures, and transformationsto support large‑scale data processing
Build and optimize enterprise data warehouse solutions on Snowflake.
Develop data ingestion, transformation, and orchestration workflows using Python, PySpark, and Airflow.
Develop and optimize complex SQL queries, stored procedures, views, streams, and tasks in Snowflake.
Implement scalable cloud‑based data solutions leveraging AWS services.
Ensure data quality, governance, security, and compliance across the data platform.
Perform query tuning, performance optimization, and cost optimization within Snowflake.
Automate deployment and monitoring through CI/CD and DevOps practices.
Collaborate with business stakeholders, architects, analysts, and engineering teams to translate business requirements into technical solutions.
Troubleshoot production issues and implement preventive measures to enhance platform reliability.
Preferred Experience
Experience in large‑scale cloud data migration initiatives.
Strong understanding of enterprise data architecture and modern data platforms.
Experience working with structured, semi‑structured, and unstructured data.
Exposure to Banking, Financial Services, or other large enterprise environments will be an added advantage.
Experience in leading technical discussions and mentoring engineering teams.
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