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Tier-1 brand, remote option, metro role and broad data engineering requirements increase applicant competition.
Skills are largely transferable across industries, but finance domain knowledge and specific tooling slightly increase selectivity.
Explicit 10–14 years plus many mandatory technologies and platform leadership increase screening strictness.
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Lead design, development, and optimization of large-scale enterprise data platform capabilities including data pipelines, ingestion, transformation, and workflow orchestration using Python, SQL, Apache Airflow, and Snowflake.
Drive platform improvements in metadata management, data quality, governance, operational efficiency, and incorporate Agentic AI capabilities to enhance automation and decision-making.
Provide L2/L3 production support, lead POCs and technology evaluations, support Agile processes, and collaborate across teams to ensure platform reliability and adoption.
Bachelor’s degree in Computer Science, Information Systems, or related technical field.
10–14 years of experience in Data Engineering, Data Platform Engineering, or related software engineering role.
Hands-on experience with Python, Apache Airflow, Snowflake (or equivalent cloud data warehouses), SQL query optimization, and dbt in modern data platforms.
Experience with cloud-native and distributed environments (Azure, AWS, GCP), streaming technologies (Snowpipe, Kafka), containerization (Docker, Kubernetes), and metadata/lineage/governance frameworks.
Senior-level data engineering professional with expertise in designing and scaling enterprise data platforms using modern cloud-native technologies and advanced data orchestration.
Experience leading complex technical design decisions, mentoring engineers, and driving delivery in Agile environments involving cross-functional collaboration.
Strong practical knowledge of AI integration in data workflows, production-grade systems engineering, and continuous platform improvement aligned with high operational standards.