Allata, LLC

• Design, develop, and maintain scalable data pipelines using Databricks (PySpark) and Python. • Build and optimize ETL/ELT processes within Azure cloud environments. • Implement data models following modern Data Lakehouse principles (e.g., Medallion architecture). • Ensure data quality, consistency, and performance across ingestion, staging, and curated layers. • Collaborate with data architects, analysts, and business stakeholders to translate healthcare data requirements into technical solutions. • Develop reusable data transformation logic and modular processing components. • Support deployment processes following CI/CD and DevOps best practices. • Monitor and optimize data workflows for performance, scalability, and reliability. • Contribute to data governance, security, and compliance practices relevant to healthcare environments.

• Lead and manage project teams to deliver high-impact solutions for clients. • Evaluate and select frameworks to solve business and technology problems on projects. • Provide clear, transparent, and accurate estimates for project-level assignments. • Generate and adjust plans for projects, ensuring timely completion of assignments. • Manage issue and risk identification/resolution processes to mitigate potential project challenges. • Drive meeting outcomes through effective preparation, execution, and follow-through. • Develop trusted relationships with clients, stakeholders, and project leadership. • Actively manage project profitability and adherence to Allata's policies and systems. • Coach team members to enhance their professional growth and performance.

• Define and evolve Snowflake platform architecture, standards, and best practices. • Translate business goals into a pragmatic technical roadmap and delivery plan. • Lead and mentor data engineers; establish quality bars, review code, and guide execution. • Manage delivery using Agile rituals; align priorities across data, analytics/BI, and application teams. • Design, build, and optimize ELT pipelines on Snowflake with an ELT-first approach (dbt preferred). • Implement modern data ingestion using tools such as Fivetran, ADF, Glue, or Matillion. • Set up orchestration and CI/CD for data using Airflow or Dagster and Git-based pipelines. • Ensure data quality, observability, monitoring, alerting, documentation, and runbooks. • Apply performance tuning and cost optimization in Snowflake, including query profiling and warehouse sizing. • Implement security and governance in Snowflake (RBAC, masking, row access policies, auditing, data sharing). • Facilitate discovery with stakeholders, clarify requirements, and communicate trade-offs and recommendations.

• Contribute to the technical strategy and architectural direction of an AI-driven product • Design and build scalable, resilient, and maintainable systems • Partner with Product and Business stakeholders to align engineering decisions with company objectives • Work across multiple languages and frameworks as required by evolving product needs • Drive technical improvements, optimization, and innovation initiatives • Provide mentorship and technical guidance to engineering peers • Support the continuous evolution of AI-powered capabilities within the platform

• Test Automation Frameworks: Design, develop, and maintain automated test frameworks and select appropriate testing tools. • Test Execution & Defect Management: Execute and maintain automated and manual test suites, create test cases, and report defects. • Quality Assurance Leadership: Lead testing initiatives, define test strategies, and promote best practices within the team. • Collaboration & Development: Work closely with developers and product owners, participate in code reviews, and support feature development. • Continuous Improvement: Implement CI testing, participate in agile ceremonies, and drive process improvements.

• Design and build internal tools and analysis workflows that leverage LLMs as assistive components. • Create reliable, explainable, and human‑validated solutions where AI enhances — but does not replace — engineering judgment. • Focus on software engineering, internal tooling, and thoughtful, pragmatic AI integration.

• Design and build internal tools and analysis workflows that leverage LLMs as assistive components. • Create reliable, explainable, and human‑validated solutions where AI enhances engineering judgment. • Focus on software engineering, internal tooling, and thoughtful, pragmatic AI integration.

• Design, develop, and maintain scalable software solutions for a Data Management Platform • Contribute to both backend and frontend development efforts • Collaborate with team members and client stakeholders to define technical solutions aligned with business requirements • Write clean, maintainable, and well-tested code following best engineering practices • Participate in code reviews, technical discussions, and continuous improvement initiatives • Support CI/CD pipelines and cloud deployments • Ensure quality through automated testing and a strong focus on reliability and performance

• Design and develop user-friendly interfaces for bulk data editing using Angular. • Utilize Java and Angular to implement user-friendly web-based UIs for importing and managing data at scale. • Collaborate closely with stakeholders to understand requirements and deliver high-quality solutions.