• Transform and build robust solutions for transferring data from first and third-party applications to and from our data warehouse • Envision and design toward industry patterns on data exchange with our enterprise clients through a mix of traditional push delivery, cloud, and event-driven (eg: API, grpc) data sharing methods. • Support implementation and data integration as new partners roll onto the Bestow platform, and recommend improvements on configurability of platform. • Making decisions as a team. The things you build will be maintained and improved upon by others; there is a shared responsibility to make defensible design considerations and high collaboration. • Champion test-first design principles, proactively writing tests before code to maintain high coverage and pipeline reliability. • Develop hardened and repeatable (CI/CD) data models and pipelines to enable reporting, modeling and machine learning. • Ensure data quality through automated monitoring and alerting, and occasionally serving within an on-call rotation. • Leverage Google Cloud (GCP) tools (eg: Cloud Run, Cloud Functions, Vertex AI, App Engine, Cloud Storage, IAM, etc.) and services (eg: Astronomer - Apache Airflow) to bring data workloads to production. • Drive and support MLOps to improve Data Science monitoring and governance • Enable and support Generative AI (eg: LLM) pipelines, allowing internal teams to quickly prototype. Support the architecture and rollout of GenAI products and features into the marketplace. • Collaborate with product, engineering, stakeholders and data teams to deliver informed solutions to platform and client needs

• Assess legacy data and reporting workloads to identify where automation replaces manual effort. • Lead the migration of Informatica-based data workloads to Databricks, ensuring performance, reliability, and data integrity. • Design and execute an automation-first modernization strategy for data pipelines, reporting systems, and analytics platforms. • Apply tool-assisted and AI-assisted techniques to accelerate modernization while maintaining compliance and control. • Build repeatable frameworks and patterns for: Data ingestion, transformation, and orchestration; Reporting and analytics modernization; Data validation, reconciliation, and quality controls. • Establish governance to ensure modernization efforts are consistent, auditable, and scalable. • Partner with distributed data teams to deploy and scale modernization patterns across environments. • Provide hands-on technical leadership while remaining engaged in execution.

• Design & implement a modern data warehouse using Amazon Redshift, ensuring scalability and performance. • Develop and maintain ETL pipelines using AWS Glue (Python/PySpark), moving data from SQL Server (RDS) and other sources. • Structure our S3 data lake for efficient storage, partitioning, and integration with Redshift. • Define data models (star schema, dimensional modeling) to support reporting and analytics. • Establish data governance—documentation, quality checks, and monitoring for pipelines. • Collaborate with BI teams to ensure the warehouse meets reporting needs (CRM, dashboards). • Optimize costs by tuning Redshift clusters, managing Glue job efficiency, and automating workflows. • Troubleshoot data infrastructure issues and optimize performance across systems. • Contribute to the long-term vision for the data platform, including new tools, workflows, and process automation. • Maintain documentation of data systems, flows, and integration strategies. • Support project planning and estimation in coordination with business and technical teams

• Design, develop, and maintain scalable data pipelines and ETL processes using Python and AWS services • Build and deploy serverless functions using AWS Lambda for data processing and automation • Implement data integration solutions connecting diverse source systems to analytics platforms • Mentor junior data engineers through pair programming, code reviews, and technical coaching • Collaborate with data analysts and business stakeholders to understand requirements and deliver solutions • Optimize data pipeline performance, reliability, and cost efficiency in AWS environments • Develop and maintain comprehensive documentation for data systems and processes • Participate in on-call rotation to support production data infrastructure • Contribute to the establishment of team coding standards and engineering best practices • Work with Git for version control and participate in collaborative development workflows