• Design, implement, and optimize Elasticsearch clusters for high-performance querying and data retrieval. • Build and manage Elasticsearch indexes, ensuring data is stored, indexed, and queried efficiently. • Build and optimize data storage solutions like data lakes and warehouses. • Integrate structured and unstructured data from various internal and external systems to create a unified view for analysis. • Ensure data accuracy, consistency, and completeness through rigorous validation, cleansing, and transformation processes. • Maintain comprehensive documentation for data processes, tools, and systems while promoting best practices for efficient workflows. • Collaborate with product managers, and other stakeholders to gather requirements and translate them into technical solutions. • Participate in requirement analysis sessions to understand business needs and user requirements. • Provide technical insights and recommendations during the requirements-gathering process. • Participate in Agile development processes, including sprint planning, daily stand-ups, and sprint reviews. • Work closely with Agile teams to deliver software solutions on time and within scope. • Adapt to changing priorities and requirements in a fast-paced Agile environment. • Conduct thorough testing and debugging to ensure the reliability, security, and performance of applications. • Write unit tests and validate the functionality of developed features and individual elements. • Writing integration tests to ensure different elements within a given application function as intended and meet desired requirements. • Identify and resolve software defects, code smells, and performance bottlenecks. • Stay updated with the latest technologies and trends in full-stack development. • Propose innovative solutions to improve the performance, security, scalability, and maintainability of applications. • Continuously seek opportunities to optimize and refactor existing codebase for better efficiency. • Stay up-to-date with cloud platforms such as AWS, Azure, or Google Cloud Platform. • Collaborate effectively with cross-functional teams, including testers, and product managers.

• Design and implement data pipelines for the collection, storage, and transformation of data from a variety of sources • Develop and maintain data models and schema to support data analysis and reporting • Write and maintain ETL jobs to extract, transform, and load data into our data warehouse • Collaborate with ML engineers, data analysts, and other stakeholders to understand data requirements and develop solutions to support data-driven decision-making • Monitor and optimize data pipelines and processes to ensure data quality and performance

• Design, build, and maintain scalable, reliable, and secure data pipelines using Python, with a focus on enabling data access and insight across product teams, engineering, and entities. • Develop reusable data services and frameworks that support high-quality data ingestion, transformation, and ML model deployment — accelerating analytics and experimentation across the organization. • Collaborate with data scientists and ML engineers to productionize machine learning workflows using SageMaker, Vertex AI, or other MLOps tools • Implement monitoring, testing, and CI/CD automation for data pipelines and ML services • Own and evolve real-time and batch data integrations between ActBlue’s core systems and user-facing applications — influencing design decisions and architecture across multiple teams including Product, Engineering, and Analytics. • Develop, optimize and support reverse ETL workflows using tools like Hightouch • Participate in code reviews, mentor junior engineers, and help foster a high-trust engineering culture. • Demonstrate technical leadership through writing documentation, establishing effective monitoring, and fostering clear and audience-oriented communication.

• Designing, building, and optimizing scalable data pipelines to process and integrate data from various sources in real-time or batch modes. • Developing and managing ETL/ELT workflows to transform raw data into structured formats for analysis and reporting. • Integrating and configuring database infrastructure, ensuring performance, scalability, and data security. • Automating data workflows and infrastructure setup using tools like Apache Airflow, Terraform, or similar. • Collaborating with data scientists, analysts, and other stakeholders to ensure efficient data accessibility and usability. • Monitoring, troubleshooting, and improving the performance of data pipelines and infrastructure to ensure data quality and flow consistency. • Working with cloud infrastructure (AWS, GCP, Azure) to manage databases, storage, and compute resources efficiently. • Implementing best practices for data governance, data security, and disaster recovery in all infrastructure designs. • Staying current with the latest trends and technologies in data engineering, pipeline automation, and infrastructure as code.