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Lead a team to build and maintain ETL pipelines, Azure Data Platform, and data storage infrastructure using PySpark, Python, SQL, and Azure Databricks.
Senior Data Engineer builds and operates scalable cloud/on-prem data architectures, pipelines, and models to store, integrate, and secure enterprise data assets for AI and analytics workloads.
Build and deploy cloud-native microservices in Python/Java on Azure, integrating data models, APIs, and Kubernetes for an insurance-focused tech stack.
Designs and documents enterprise Java microservices architecture for insurance systems using Spring Boot, Kubernetes, and DevOps practices.
Designs and owns scalable AI architectures for enterprise clients, blending Azure cloud solutions, Power Platform low-code tools, and agentic AI frameworks like LangGraph and RAG systems.
Build AI-powered data platform tools (React UIs, Python APIs, vector stores) that let users explore and query financial data in natural language while ensuring security and correctness in a regulated environment.
Design and implement scalable data platforms (lakehouses, data lakes) and AI foundations for enterprise customers, focusing on governance, quality, and modernization.
Designs and builds scalable data pipelines for finance and supply chain systems, ensuring reliable data ingestion and transformation in a warehouse and data lake environment.
Build and optimize cloud-native data pipelines and warehouses on AWS, using Python, Spark, and modern lakehouse practices to migrate and modernize customer data platforms.
Senior Data Engineer builds and maintains Azure-based data pipelines, Power BI reports, and AI solutions for clients, troubleshooting issues and mentoring junior team members.
Design and build scalable, cloud-native data pipelines and data-lake architectures on AWS using S3, Glue, Redshift, and Kinesis.
Designs scalable Delta Lake data architectures with ACID transactions and time-travel queries, building ETL/ELT pipelines using Spark and Parquet storage.
Designs and builds Azure-based data pipelines and analytics solutions using Python and SQL, mentors teammates, and collaborates on data-centric applications.
Design and maintain data pipelines, lakes, and governance for a large bank’s mortgage servicing unit, ensuring accuracy and security while collaborating with engineers and analysts.
Lead a team to design and build ETL pipelines and AWS data lakes using PySpark and Python, integrating data from SAP and SQL sources.
Own and improve data pipelines for JPMorgan’s Home Lending Servicing, ensuring accurate, secure, and accessible data for analytics and operations using SQL, Python, and AWS.
Designs and builds cloud-native data platforms on AWS, focusing on scalable ETL/ELT pipelines and data lake architectures for reporting-ready datasets.
Design and build scalable AWS data pipelines and real-time streaming systems using Confluent Kafka for analytics and AI workloads.
Builds and maintains scalable Azure data pipelines and lakes using Data Factory, Data Lake, and SQL Database with Python/SQL.
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