• Analyse and ingest new datasets from varied sources • Design, build, and support cloud infrastructure • Design, build, and support AI and ML processes and infrastructure • Design and build modern, metadata driven data pipelines and data streams • Design and build data service APIs • Transform and cleanse complex datasets • Analyse current business practices, processes and procedures • Implement effective metrics and monitoring processes

• Develop and maintain enterprise data models aligned with federal and state standards. • Design data integration strategies across multiple systems and agencies. • Ensure compliance with FISMA, FedRAMP, NIST, and applicable state data security frameworks. • Serve as a trusted advisor to federal and state clients, translating business needs into technical solutions. • Lead customer workshops, requirements sessions, and architecture reviews. • Communicate complex technical concepts clearly to non-technical stakeholders. • Establish data governance policies, metadata management, and stewardship practices. • Implement data quality frameworks and monitoring processes. • Architect solutions leveraging AWS, Azure, or GCP for government workloads. • Support migration from legacy systems to modern cloud-based platforms. • Partner with federal and state stakeholders, program managers, and technical teams to align data strategies with mission objectives. • Mentor junior architects and data engineers.

• Conduct technical screens and deep-dives for data/AI roles: SQL, Python, data modeling, ML theory, and applied system design. • Assess practical skills: feature engineering, evaluation metrics, leakage detection, experiment design, and tradeoff analysis. • Evaluate production readiness: monitoring, drift detection, CI/CD for ML, deployment patterns. • Provide clear, rubric-based feedback and participate in calibration and leveling. • Improve question banks, rubrics, and interview loops for data and AI roles.

• Understand Cogstate data sources and develop data pipelines using Databricks to bring all data into the data lake. • Design, develop, implement, and tune large-scale distributed systems and pipelines that process large volumes of data; focusing on scalability, low-latency, and fault-tolerance in every system. • Developing scalable and re-usable frameworks for ingesting data into Azure Databricks, incorporating standards and best practices into engineering solutions. • Databricks engineering - query tuning, performance tuning, troubleshooting, and debugging pipelines. • Deep understanding of ETL/ELT design methodologies, architecture, strategy, and tactics for complex ETL solutions, including CI/CD skills. • Develop high performance scripts in PySpark to achieve objectives of enterprise data, BI, data visualization and analytics needs. • Data processing/transformation using various technologies such as Apache Spark, SQL, Python/Scala and Azure cloud services. • Manage code versions in source control and coordinate changes across teams by leveraging Github. • Participate in architecture design and discussions, provide logical and physical data design, and database modelling. • Be part of the Agile team to ensure availability of data to internal and external users. • Organize and manage data shares. • Solve complex data issues around data integration, data quality, and other data processing incidents. • Work with business system owners to resolve source data issues and refine transformation rules.