• Architect and develop large-scale, mission-critical BI and data platform solutions serving millions of users across the globe, leveraging AWS native technologies including Athena, Redshift, Glue, QuickSight, and S3. • Lead the design and implementation of robust data pipelines, data lakes, and data warehouses using modern architectures (Iceberg, Parquet, columnar formats) to support real-time and batch analytics at scale. • Drive technical strategy and architectural decisions for the BI platform, including data modeling, query optimization, performance tuning, and cost optimization across AWS services. • Build and maintain sophisticated back-end services, ETL/ELT workflows, and front-end analytics applications using Python, SQL, React, and modern web technologies. • Design and implement efficient data storage solutions across relational databases (Redshift, PostgreSQL) and non-relational databases (DynamoDB, S3), ensuring optimal performance and cost-efficiency. • Develop and maintain REST APIs and event-driven architectures to enable seamless integration between data services, analytics tools, and customer-facing applications. • Serve as the technical lead and mentor for engineering teams, conducting architecture reviews, code reviews, and providing guidance on complex technical challenges. • Collaborate with cross-functional teams including data engineers, analytics engineers, product managers, and DevOps to deliver innovative BI solutions that drive business value. • Champion engineering excellence by establishing best practices, design patterns, and coding standards for data-intensive applications. • Lead Agile ceremonies, drive sprint planning, and ensure timely delivery of high-quality software solutions while maintaining technical debt at manageable levels. • Evaluate and integrate emerging AWS services and open-source technologies to continuously improve platform capabilities and developer productivity. • Troubleshoot and resolve complex performance issues in distributed data systems, optimizing query performance, data processing workflows, and infrastructure costs. • Participate in strategic planning and roadmap development, translating business requirements into scalable technical solutions. • Contribute to the team on-call rotation, providing expert-level support for production environments and mentoring team members on incident response.

• Serve as the expert for ETL and DB solutions, collaborating with business stakeholders and IT teams to define requirements, gather data, and implement optimized data solutions. • Design, implement, and maintain data systems in Snowflake to ensure data scalability and accessibility. • Implement and manage data lakes and data warehouses, creating pipelines and data models to enable efficient analytics and reporting. • Establish and document strategies for managing data transfer processes, including secure file transfers (SFTP), batch data processing, and real-time streaming. • Build and optimize ETL pipelines for data extraction, transformation, and loading into operational databases or analytical platforms. • Integrate and support data visualization tools such as Power BI, Sisense, Google Looker, Tableau, or similar platforms to enable actionable insights for business stakeholders. • Develop and maintain optimized data models for dashboards and reporting, ensuring compatibility with visualization tools. • Plan, coordinate, and implement database migrations, upgrades, and patches with minimal downtime. • Define and enforce database governance policies, including data integrity, security, and compliance with regulatory requirements. • Analyze and resolve database performance issues by optimizing queries, indexes, and schema designs. • Partner with vendors to evaluate, select, and implement database tools, services, and technologies; stay informed about product roadmaps and industry trends. • Develop disaster recovery and high-availability solutions, including replication, clustering, and failover.

• Design and implement data pipelines (ETL/ELT) using modern tools (e.g., Apache Airflow, DBT, Dataflow); • Integrate data from transactional systems, APIs, and relational and non-relational databases; • Create and maintain optimized data structures in analytical environments (data lakes and data warehouses); • Ensure data governance, data quality, and data cataloging; • Automate routines for data extraction, transformation, and loading; • Support data scientists, analysts, and product squads with reliable, well-modeled data; • Participate in modernization and data migration projects to the cloud; • Monitor and resolve failures in pipelines and other critical data processes.

• Supports ECS/EAD (Enterprise Case Selection/Enterprise Anomaly Detection) data engineering tasks including ingestion scripting, basic transformation logic, and data preparation activities for analytics and operational workloads. • Assists senior engineers in building and maintaining pipelines, validating data accuracy, and troubleshooting low‑complexity ingestion errors. • Performs initial schema mapping, data profiling, and documentation updates. • Works with analysts and data scientists to ensure datasets are complete, accessible, and aligned with ECS/EAD mission and compliance requirements