Senior Engineer, Data

About this role

Bellese is a mission-driven Digital Services Company committed to pioneering innovative technology solutions in civic healthcare. Our dedication lies in making a meaningful impact on public health outcomes.    Driven by service design, we strive to know the “Why” to understand the healthcare journey for patients, caregivers, providers, payers, and policymakers. Our goal is to design and build solutions that reduce confusion, provide clarity, support decision making, and streamline the process so that we and our partners can focus on providing better health outcomes by improving patient care and reducing costs and burden. As a Senior Engineer: You contribute meaningfully to the success of one or more projects or products, helping drive positive outcomes. You demonstrate proficiency and specialized skill in one or more key technologies relevant to the team’s goals. You accurately scope, estimate, design, and deliver medium-to-large features while actively reducing technical debt across projects or products. You proactively support and collaborate with team members, fostering a positive and productive team environment. You adhere to established team processes, ensuring a steady and reliable delivery of features to production. These are the types of things you’ll be working on  Cross-functional Collaboration: Work closely with teammates, external teams, and client stakeholders to ensure quality considerations are integrated at every stage of the SDLC.  Test Planning and Design: Develop comprehensive test plans, test cases, and relevant documentation that align with software specifications and user requirements to ensure thorough testing of features and functionalities. Bug Tracking and Resolution: Utilize bug-tracking tools to document, prioritize, and track defects. Facilitate communication across teams to ensure timely and effective issue resolution. Quality Metrics and Standards: Establish and enforce standards and best practices. Track metrics to assess the quality of the software throughout the SDLC. Automate Testing Processes: Focus on increasing test coverage, speed, and accuracy to support continuous integration and deployment pipelines. Continuous Improvement: Continuously evaluate and recommend new QA tools, frameworks, and techniques, driving innovation and staying up-to-date with industry trends and best practices Compliance and Regulatory Adherence: Ensure testing procedures comply with industry standards and regulatory requirements, preparing documentation for audit purposes. Performance and Security Testing: Implement and conduct performance and security testing following best practices and business requirements. Must have Advanced SQL. Window functions, CTEs, and the ability to read a query plan and act on it. You have tuned queries against tables large enough that a bad join was a real problem. PostgreSQL Schema design, indexing strategy, and migrations in a live system. Pipeline Ownership. Responsibility for the pipeline - Github Actions, Jenkins or similar Python. This is the working language of the role — ingestion handlers, validation logic, reconciliation tooling. Fluency, not familiarity. A validation and reconciliation instinct. You can describe how you'd prove two sources agree when neither one is authoritative, and you've had to do it. Comfort with data you don't control. Third-party feeds break, change format without notice, and arrive late. You design for that rather than being surprised by it.     Nice to have Event-driven ingestion on AWS specifically (Lambda, S3 events, EventBridge, Step Functions). Equivalent experience on other stacks transfers - we care that you've built it, not where. Pharmacy, drug pricing, formulary, or payer data experience. Direct experience with any of NCPDP, First Databank, Medi-Span, or HPMS data is a strong differentiator; we do not expect all of them. Orchestration tooling: Airflow, Dagster, dbt, or Step Functions. IAC (Infrastructure as code) - Terraform, CloudFormation, or CDK. Experience delivering in a FedRAMP or ATO-governed environment. Data quality frameworks (Great Expectations, Soda, or homegrown equivalents). Applied AI/ML work, particularly for anomaly detection on data feeds.