• Lead and manage daily and long-term operations of the Data Engineering team. • Oversee enterprise data pipelines, ETL/ELT processes, analytical platforms, and governance workflows. • Ensure adherence to enterprise data architecture, integration standards, governance practices, and security requirements. • Prioritize work across competing institutional demands while balancing service reliability and operational risk. • Guide the development of scalable data models, semantic layers, and metadata documents. • Coordinate incident response, data quality remediation, and continuous improvement initiatives. • Mentor staff in professional development, engineering best practices, and service delivery excellence. • Collaborate across IT and institutional leadership to support enterprise data initiatives.

• More than 12+ years of IT experience • Microsoft Fabric (OneLake, Lakehouse, Data Factory, Power BI),Databricks (data engineering, SQL, notebooks, app/backend integration patterns)Enterprise data warehouses and lakehouse platforms (Snowflake, Synapse, BigQuery – experience with one or more) • Deep knowledge of data modeling, data warehousing, and lakehouse patterns • Hands‑on experience with at least one modern data platform: • Microsoft Fabric and/or Databricks strongly preferred • Experience integrating data platforms with applications using APIs, services, or event‑driven patterns • Solid understanding of cloud architecture concepts (security, networking, scalability, cost management) • Strong communication skills with the ability to engage both technical and business stakeholders • Experience working in client‑facing or consulting environments

• Plan and align the project with the Androidization strategy • Gather and validate functional and technical requirements • Design the solution architecture and the data model • Automate integration and processing of operational data using Python • Model and automate refined tables incorporating business rules • Implement monitoring and proactive alerts • Publish and validate the tables in the production environment • Document the entire technical and functional platform architecture • Provide training and formalize the technical handover

• Design, build, and optimize Snowflake-centric data architectures to support enterprise analytics, reporting, and operational use cases • Own dbt transformation layers, including model design, testing, documentation, and deployment best practices • Implement scalable data modeling patterns (star schemas, data vault, dimensional models) aligned to business needs • Develop and maintain reliable data pipelines integrating sources such as Salesforce, application databases, and external client data • Ensure data quality through validation, testing, monitoring, and observability frameworks • Optimize Snowflake performance and cost through query tuning, warehouse design, and efficient data modeling • Partner closely with Analytics, BI, Product, and Engineering teams to deliver trusted, analytics-ready datasets • Contribute to architectural standards, code reviews, and best practices across the CDP team • Document data flows, models, and platform decisions to support long-term scalability and knowledge sharing • Ensure data pipelines and models meet PHI / PII / HIPAA compliance requirements • Support secure access patterns, role-based permissions, and data governance controls