• Design, implement, and continuously expand data pipelines by performing extraction, transformation, and loading (ETL) activities • Gather requirements and business process knowledge to transform data in ways that meet end-user needs • Create new SDLC processes as needed and maintain and improve existing processes • Ensure data architecture is scalable and maintainable • Collaborate with business stakeholders and colleagues to design and deliver accurate, high-quality data • Investigate data to identify potential issues within ETL pipelines, notify end users, and propose effective solutions • Prepare and maintain documentation for future reference • Additional duties as assigned

• Build, scale, and maintain robust data solutions to support the firm's objectives. • Implement and optimize high-performance data pipelines: extraction, loading, transformation, and orchestration – that are designed for scalability, reliability, maintainability, and speed. • Lead software development projects end to end involving large language models (LLMs), retrieval-augmented generation (RAG) frameworks, and other AI technologies. • Champion modern software engineering practices as CI/CD, infrastructure-as-code, containerization, and cloud-native deployments. • Collaborate closely with business stakeholders to transform use cases into production-ready services and solutions, owning the system from concept to production. • Implement rigorous testing and monitoring practices to maintain superior data quality and integrity. • Mentor and develop junior team members, fostering a culture of excellence and continuous learning within the team. • Be willing to travel up to 20% of the time to collaborate with distributed team members across locations.

• Design, implement, and optimize data pipelines using both batch and streaming processing frameworks. • Architect and maintain data lakehouse solutions using Apache Iceberg and object storage such as S3. • Implement scalable Data Vault and Star Schema models. • Build and manage real-time ingestion pipelines with Kafka, Spark, or Flink. • Integrate and orchestrate workflows using tools like Airflow, dbt, NiFi, or Airbyte. • Enforce data governance, data quality, and access control policies. • Troubleshoot pipeline performance and reliability issues.

• Develop and maintain enterprise-level data structures, including conceptual, logical, and physical models. • Define and implement data strategies, standards, and governance frameworks to ensure quality, security, and compliance. • Design, implement and oversee data integration processes, including ETL queries and workflows, APIs, and data pipelines. • Design, construct, and maintain data warehouses to support IS-Development process data optimization, analytics and reporting. • Build and deploy dashboards and reports using SQL and Power BI. • Conduct advanced analytics, including regression analysis and simulations, to drive strategic decisions. • Partner with business stakeholders, IT teams, and leadership to promote data-driven decision-making and operational efficiency. • Assess and recommend tools, platforms, and technologies for data management, integration and analytics. • Monitor and optimize database performance, scalability, and reliability. • Identify and develop key performance indicators and ensure data accuracy, consistent monitoring and reporting.