Location:- Washington DC under 50 miles Skype hire/Need LinkedIn • At least ten or more years of experience in AI, Data Science, Software Engineering experience, including knowledge of Data ecosystem • Bachelor’s degree in Computer Science, Information Systems, or other related field is required or related work experience • Data Modeling: Expertise in designing and implementing data models optimized for storage, retrieval, and analytics within Databricks on AWS, including conceptual, logical, and physical data modeling • Databricks Proficiency: In-depth knowledge and hands-on experience with AWS Databricks platform, including Databricks SQL, Runtime, clusters, notebooks, and integrations. • ELT (Extract, Load, Transform) Processes: Proficiency in developing ETL pipelines to extract data from various sources, transform it as per business requirements, and load it into the central data lake using Databricks tools and Spark • Data Integration: Experience integrating data from heterogeneous sources (relational databases, APIs, files) into Databricks while ensuring data quality, consistency, and lineage • Performance Optimization: Ability to optimize data processing workflows and SQL queries in Databricks for performance, scalability, and cost- effectiveness, leveraging partitioning, clustering, caching, and Spark optimization techniques • Data Governance and Security: Understanding of data governance principles and implementing security measures to ensure data integrity, confidentiality, and compliance within the centralized data lake environment • Advanced SQL and Spark Skills: Proficiency in writing complex SQL queries and Spark code (Scala/Python) for data manipulation, transformation, aggregation, and analysis tasks within Databricks notebooks • Cloud Architecture: Understanding of cloud computing principles, AWS architecture, and services for designing scalable and resilient data solutions • Data Visualization: Basic knowledge of data visualization tools (e.g. Tableau) to create insightful visualizations and dashboards for data analysis and reporting purposes Familiarity with government cloud deployment regulations/compliance policies such as FedRAMP, FISMA, etc. • Leverage financial industry expertise to define conceptual, logical and physical data models in Databricks to support new and existing business domains • Work with product owners, system architects, data engineers, and vendors to create data models optimized for query performance, compute and storage costs • Define best practices for the implementation of the Bronze/Silver/Gold data layers of the lakehouse • Provide data model documentation and artifacts generated from data, data dictionary, data definitions, etc