Komodo Health-posted 2 days ago
$179,000 - $277,000/Yr
Full-time • Mid Level
Hybrid
501-1,000 employees

We Breathe Life Into Data At Komodo Health, our mission is to reduce the global burden of disease. And we believe that smarter use of data is essential to this mission. That’s why we built the Healthcare Map — the industry’s largest, most complete, precise view of the U.S. healthcare system — by combining de-identified, real-world patient data with innovative algorithms and decades of clinical experience. The Healthcare Map serves as our foundation for a powerful suite of software applications, helping us answer healthcare’s most complex questions for our partners. Across the healthcare ecosystem, we’re helping our clients unlock critical insights to track detailed patient behaviors and treatment patterns, identify gaps in care, address unmet patient needs, and reduce the global burden of disease. As we pursue these goals, it remains essential to us that we stay grounded in our values: be awesome, seek growth, deliver “wow,” and enjoy the ride. At Komodo, you will be joining a team of ambitious, supportive Dragons with diverse backgrounds but a shared passion to deliver on our mission to reduce the burden of disease — and enjoy the journey along the way. The Opportunity at Komodo Health Quality is at the heart of everything that Komodo Health does, underpinning our vision to reduce the global impact of disease. As Komodo scales its leading Healthcare Map® and AI-driven applications, the need for a unified, measurable standard of trust is paramount. We are not looking for a traditional Data Quality Engineer. We are seeking a technical expert with business acumen to bring a clear perspective to guide the definition, creation, and adoption of the future of data quality across Komodo Health. This role will be a catalyst for enterprise transformation, replacing fragmented quality metrics with a single, implementable standard: Data Reliability. You will be empowered to take the strategic vision of the leadership team, prototype it, advocate for it, and drive its adoption across engineering, product, and data science teams. Pivotal Mission & Strategic Mandate The Staff Data Reliability Architect will own the execution and organizational adoption of three critical enterprise initiatives: Redefine the North Star: Lead the conceptual shift from general "data quality" to Data Reliability across the entire organization (is the correct data present at the correct place at the correct time). Build the Source of Truth: Design, prototype, and champion a single, centralized "Data Reliability Source of Truth" platform to measure and display reliability KPIs at every key stage of the data delivery pipeline. Automate Frameworks: Create the technical framework and reference architecture necessary to automate the creation, deployment, and monitoring of Data Reliability checks throughout the product development lifecycle.

  • Strategic Partnership: Serve as a strategic thought partner to the Head of Data Quality on all matters related to enterprise data reliability and quality assurance architecture.
  • Vision Execution & Prototyping: Rapidly translate executive vision into tangible prototypes (e.g., proof-of-concept dashboards, reference pipeline code) and scalable technical roadmaps.
  • Executive Evangelism & Buy-in: Act as the Chief Evangelist for the Data Reliability metric, tirelessly building conviction across executive, product, and engineering leadership, presenting a clear perspective with conviction
  • Organizational Adoption: Develop and lead change management programs to ensure seamless adoption of the new Data Reliability standard and Source of Truth platform by development teams.
  • Progress Tracking & Reporting: Define the OKRs and KPIs for the Data Reliability initiative and proactively create and deliver executive-level updates on progress, challenges, and ROI.
  • Best Practices Evangelist: Formalize, package, and teach "best of the best" architectural and coding approaches for Data Reliability engineering to engineering teams organization-wide.
  • Architectural Ownership: Design the end-to-end architecture for the centralized "Data Reliability Source of Truth," ensuring it is highly scalable, performant, and consumable via APIs/dashboards.
  • Framework Development: Build and implement highly repeatable, configurable technical frameworks (using Python/SQL/Spark) to automate the deployment of Data Reliability checks into CI/CD pipelines and data transformation jobs.
  • Data Modeling: Drive the modeling and schema design of the core reliability tables, ensuring data from disparate data stages (raw, curated, derived) can be standardized and measured effectively.
  • AI/ML Alignment: Ensure that the defined Data Reliability metrics are explicitly fit-for-purpose for our AI/ML models, providing the assurance needed for feature engineering and model deployment.
  • Mentorship & Coaching: Provide technical coaching to existing Data Quality Engineers on advanced techniques required for building enterprise-scale reliability frameworks.
  • Leadership Presence: Proven ability to create, articulate, and successfully sell a strategic technical vision to senior executive and diverse technical audiences.
  • High Initiative: Demonstrated history of taking complex, ambiguous problems and independently driving them to concrete, measurable solutions.
  • Persistence: The mental resilience and conviction necessary to pursue goals despite initial pushback or organizational inertia.
  • Experience: 12+ years of cumulative experience in Data Engineering, Data Architecture, or Platform Engineering, with a strong, dedicated focus on enterprise data quality/governance/testing.
  • Strategic DQM Focus: 5+ years of experience leading or designing enterprise-wide, multi-team data quality frameworks, not just building individual checks.
  • Technical Deep Dive: Expert-level proficiency in SQL and Python for efficient and complex data manipulation, engineering, and testing.
  • Platform Expertise: Extensive experience designing and developing with distributed data processing platforms like Spark and pipeline orchestration tools like Airflow.
  • Data Environments: Deep knowledge of modern cloud data warehousing environments (ideally Snowflake on AWS or similar MPP systems) and robust data modeling practices.
  • AI Context: Practical experience ensuring data is prepared and validated for AI/ML model consumption.
  • comprehensive health, dental, and vision insurance
  • flexible time off and holidays
  • 401(k) with company match
  • disability insurance and life insurance
  • leaves of absence in accordance with applicable state and local laws and regulations and company policy
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