• Plan, create, and maintain data architectures, ensuring alignment with business requirements. • Obtain data, formulate dataset processes, and store optimized data. • Identify problems and inefficiencies and apply solutions. • Determine tasks where manual participation can be eliminated with automation. • Identify and optimize data bottlenecks, leveraging automation where possible. • Create and manage data lifecycle policies (retention, backups/restore, etc). • In-depth knowledge for creating, maintaining, and managing ETL/ELT pipelines. • Create, maintain, and manage data transformations. • Maintain/update documentation. • Create, maintain, and manage data pipeline schedules. • Monitor data pipelines. • Create, maintain, and manage data quality gates (Great Expectations) to ensure high data quality. • Support AI/ML teams with optimizing feature engineering code. • Expertise in Spark/Python/Databricks, Data Lake and SQL. • Create, maintain, and manage Spark Structured Steaming jobs, including using the newer Delta Live Tables and/or DBT. • Research existing data in the data lake to determine best sources for data. • Create, manage, and maintain ksqlDB and Kafka Streams queries/code Data driven testing for data quality. • Maintain and update Python-based data processing scripts executed on AWS Lambdas. • Unit tests for all the Spark, Python data processing and Lambda codes. • Maintain PCIS Reporting Database data lake with optimizations and maintenance (performance tuning, etc). • Streamlining data processing experience including formalizing concepts of how to handle lake data, defining windows, and how window definitions impact data freshness.

• Build and scale robust ETL/ELT pipelines to integrate diverse data sources into a unified, high-availability ecosystem. • Develop and maintain high-performance APIs using FastAPI to expose data insights to internal services and stakeholders. • Lead data transformations by using DBT to ensure our warehouse is structured, modular, and ready for high-level analysis. • Optimise data structures and schemas to ensure lightning-fast query performance for analytical and operational use cases. • Automate data validation and cleansing processes to guarantee the highest standards of data integrity and security. • Drive technical innovation by implementing modern frameworks that enhance our processing capabilities and data reliability.

• Building and optimizing data pipelines • Integrating Aladdin datasets • Developing Snowflake SQL/ETL processes • Ensuring high‑quality, scalable data solutions

• Provide technical leadership and big-picture oversight for the National Background Investigation Services (NBIS) and Enterprise Information Technology (EIT) application development data pipelines • Articulate the data architecture strategy, conceptualize and experiment with alternative data processing approaches, and validate data models • Bridge the gap between legacy data stores and modern, cloud-native data streams within AWS GovCloud • Collaborate with cross-functional software teams, lead data transformation efforts, and exercise wide latitude for independent judgment • Conceptualize, experiment with, and articulate alternative technical approaches to data architecture • Create data pipeline, ETL/ELT, schema, and interface specification documents • Provide technical and big-picture oversight for the development of new or enhanced data source integrations • Lead the modernization of legacy data structures into cloud-native data lakes and data warehouses • Deeply engage with automated orchestration tools and secure data streaming • Drive CI/CD automation and DevSecOps practices within the data tier • Partner with software engineers, data scientists, product managers, and customer application experts to design high-performance data solutions • Leverage Jira, Confluence, and Agile SAFe v6.0 processes to translate high-level client requirements into actionable data user stories, technical roadmaps, and features.