Abacus Insights

• Own day-to-day technical health of assigned client environments, ensuring stability, performance, and SLA adherence. • Lead investigation and resolution of complex technical issues, including escalation management. • Own Root Cause Analyses (RCAs), including clear documentation of root cause, impact, and corrective actions. • Define and lead client UAT strategy, execution, and sign-off. • Coordinate testing activities across DataOps, QA, Engineering, and client teams. • Drive defect triage and resolution, ensuring alignment on severity, priorities, and exit criteria. • Support interoperability and regulatory testing (e.g., FHIR-based data, file and API validation). • Build trusted working relationships with client technical and operational stakeholders. • Act as the voice of the customer, translating client needs and feedback into actionable internal insights. • Ensure clients understand and effectively use Abacus products to achieve business outcomes. • Lead through influence across Product, Engineering, DataOps, QA, and Client Success teams. • Contribute to the evolution of support, testing, and delivery playbooks. • Identify opportunities to improve efficiency through standardization, automation, and tooling.

• Act as a senior technical escalation point during production incidents • Lead real-time incident triage, mitigation, and recovery efforts • Drive root cause analysis (RCA) with a focus on systemic, long-term fixes • Identify recurring failure patterns and push for architectural or operational improvements • Partner with Customer Success and Engineering to manage customer impact during incidents • Own post-launch reliability, stability, and operational quality of core systems • Investigate and resolve complex field issues and production defects • Ensure fixes developed during incidents or customer escalations are up streamed into the core product • Improve operational readiness of services through better runbooks, monitoring, and alerting • Reduce operational toil by converting repeated manual work into automation • Engage directly with strategic customers to solve real-world, production-grade technical challenges • Support complex deployments, integrations, and escalations in customer environments • Act as a trusted technical partner to customers during high-impact issues • Translate customer learnings into concrete product, platform, and operational improvements • Write production-quality code to: • Automate operational workflows • Improve reliability and observability • Eliminate manual intervention and reduce incident frequency • Contribute primarily in Python, with exposure to JVM-based systems as needed • Review code with a strong emphasis on operability, resiliency, and maintainability • Advocate for “build it so it can be operated” engineering standards • Provide technical leadership without formal authority, influencing design and operational decisions • Mentor engineers through pairing, reviews, and incident leadership • Collaborate closely with Product, Engineering, Data, and Customer teams • Operate effectively in high-pressure, ambiguous environments, especially during customer-impacting incidents

• Author the project architecture design documents, including data flows, sequence diagrams and overall system architecture, that will drive the technical implementation of the solutions. • Work across business and IT teams to understand the current business and system capabilities and determine the desired state to define the overall technology strategy roadmap • Work closely with cross-functional teams, including business experts, technical business analysts, project managers, sales / account executives and data engineers to successfully implement client projects which reduce technology costs and solution redundancy. • Collaborate with Product Management to identify client-specific requirements that may translate into an existing standard product feature or a future feature on the Abacus product roadmap. • Define the solution architecture visions for designated components that describe the long-term goals as well as near-term needs of the client. • Provide mentorship to internal teams on business needs and use cases for seamless client implementation. Provide ongoing support to address questions and issues raised by the implementation team.

• Build and ship production-grade, automated validation frameworks, test harnesses, and evaluation pipelines across the AI lifecycle (design → deploy). • Design and evolve an AI testing platform integrated with Databricks and MLflow, enabling repeatable testing, traceability, and auditability. • Create large-scale, scenario-based test suites (hundreds to thousands of cases) to validate agentic workflows end-to-end, including edge cases, long-tail scenarios, and failure modes. • Validate orchestration behavior (tool use, memory, decision logic) and stress-test non-deterministic system behavior before production. • Embed quality by design: define system contracts, guardrails, and safe-degradation patterns at key boundaries. • Define measurable quality signals for LLM systems (grounding, hallucinations, relevance, latency, cost) and integrate them into CI/CD pipelines as automated quality gates. • Ensure AI validation runs automatically on model, prompt, and code changes—enabling continuous quality enforcement. • Build reusable libraries and components so teams can adopt consistent AI quality practices quickly. • Own aspects of AI release readiness, including defining go/no-go criteria based on measurable quality thresholds. • Partner with AI, platform, security, and delivery teams to translate mission needs into clear quality criteria, tradeoffs, and confidence levels.

• Lead, coach, and grow DevOps, Infrastructure, and Release Engineering teams across geographies • Establish clear ownership and accountability across CI/CD, environments, and production readiness • Conduct regular 1:1s, performance reviews, goal setting, and career development planning • Hire, onboard, and retain senior DevOps and release engineering talent • Foster a culture of operational ownership, blameless postmortems, and high availability • Own the release engineering strategy for a SaaS platform supporting frequent, reliable deployments • Design and continuously improve CI/CD pipelines that support: • Trunk-based or hybrid branching strategies • Automated testing gates (unit, integration, security, performance) • Progressive delivery (feature flags, canaries, blue-green deployments) • Establish deployment standards, change classification, and risk levels across teams • Reduce release toil, manual steps, and human error through automation • Drive improvements in deployment frequency, change failure rate, and mean time to recovery (DORA metrics) • Ensure consistent release processes across services, platforms, and cloud environment • Provide technical leadership across AWS, Azure, and GCP environments • Ensure infrastructure and delivery pipelines follow security, compliance, and cloud best practices • Partner with Architecture and Security to enforce: • Infrastructure as Code (Terraform, CloudFormation, ARM/Bicep) • Secure-by-default deployments • Policy-as-code and guardrails • Influence architecture decisions to improve operability, scalability, and release safety • Own production readiness standards and release go/no-go criteria • Ensure clear incident response processes, escalation paths, and on-call readiness • Partner deeply with Product, QE, Security, and Engineering leaders to align on release expectations

Role Description We are looking for a highly technical and hands-on Senior Healthcare Data Modeler to lead a global data modeling team responsible for transforming conceptual and logical data models into optimized physical models. This role requires a deep understanding of healthcare data domains (Claims, Pharmacy, Membership, Enrollment, Provider, Billing, Interoperability - FHIR, HL7, ADT, CCDA, etc.) and expertise in enterprise data modeling best practices. The Senior Healthcare Data Modeler will work closely with the Director of Data Modeling to define data modeling standards, metadata governance, and data architecture strategies that align with business and technical needs. They will also collaborate with engineering, client implementation teams, and data governance teams to ensure models support operational and analytical use cases, high performance, and data integrity. This is an exciting opportunity for a technical leader with expertise in Databricks, Snowflake, schema evolution, and modern data engineering practices to shape the future of healthcare data platforms. Your day to day - Data Modeling Leadership & Best Practices - Lead a global team of data modelers to build scalable, high-performance physical data models aligned with enterprise architecture and industry standards. - Define and enforce data modeling best practices, including schema evolution, lossless data modeling, metadata management, and data versioning. - Convert conceptual and logical data models into optimized physical models using enterprise data modeling tools (Erwin, ER/Studio, DBT, or similar). - Ensure models support analytical (data science, actuarial, reporting) and operational (Claims, Care Management) use cases. - Maintain metadata, business glossary, and data dictionaries to support data lineage and governance. - Implement data quality rules and validation frameworks to meet industry SLAs and compliance requirements (HIPAA, HITRUST, CMS regulations). - Technical Data Modeling Engineering - Own data modeling workflows, including version control, schema deployment, and upgrade automation. - Own metadata tagging and data quality rules implementation for healthcare data domains. - Work with engineering teams to implement data models in Databricks, Snowflake, and modern cloud architectures. - Optimize data models for query performance, indexing, partitioning, and storage efficiency. - Ensure data enrichment (standardization, transformations, algorithms, and grouping) is seamlessly integrated into the data models. - Design and document data delivery patterns using complex events or business rules to enable real-time and batch processing. - Implement data federation strategies, ensuring interoperability between enterprise data lakes, cloud data warehouses, and transactional systems. - Collaboration & Client Engagement - Collaborate with Director of Data Modeling and client implementation teams to understand client needs, assess data model impact, and enhance models for efficient implementations. - Partner with engineering and product teams to define and prioritize data model features in the product roadmap. - Work with client management teams to provide support and technical expertise during client engagements. Qualifications - 8+ years of experience in data modeling, enterprise data architecture, and cloud-based data engineering. - 4+ years leading data modeling teams in enterprise environments. - Expertise in enterprise data modeling tools (Erwin, ER/Studio, DBSchema, or similar). - Strong hands-on experience with cloud data platforms (Databricks, Snowflake) and data lakehouse architectures. - Deep understanding of healthcare data models across Core Payer (Claims, Pharmacy, Membership, Enrollment, Provider, Billing, Premium) and Clinical Interoperability (FHIR, HL7, ADT, CCDA, etc.) domains. - Experience with schema evolution, lossless data modeling, and data versioning strategies. - Strong background in SQL, performance tuning, and query optimization for large-scale datasets. - Hands-on experience with data engineering workflows, including data pipeline integration, ELT/ETL optimizations, and data transformation frameworks. - Ability to mentor junior modelers, enforce modeling best practices, and drive adoption of enterprise data standards. - Excellent collaboration skills to work with data architects, engineers, and client implementation teams. Requirements - Experience with OBT, data Lakehouse, and metadata management frameworks. - Familiarity with business intelligence (BI) and reporting solutions on modern cloud data warehouses. - Knowledge of APIs, SDKs, and data distribution frameworks for enabling real-time and batch data access. - Experience with data governance, security, and regulatory compliance in healthcare data management. Benefits - Unlimited paid time off – recharge when you need it - Work from anywhere – flexibility to fit your life - Comprehensive health coverage – multiple plan options to choose from - Equity for every employee – share in our success - Growth-focused environment – your development matters here - Home office setup allowance – one-time support to get you started - Monthly cell phone allowance – stay connected with ease Our Commitment as an Equal Opportunity Employer As a mission-led technology company helping to drive better healthcare outcomes, Abacus Insights believes that the best innovation and value we can bring to our customers comes from diverse ideas, thoughts, experiences, and perspectives. Therefore, we dedicate resources to building diverse teams and providing equal employment opportunities to all applicants. Abacus prohibits discrimination and harassment regarding race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. At the heart of who we are is a commitment to continuously and intentionally building an inclusive culture—one that empowers every team member across the globe to do their best work and bring their authentic selves. We carry that same commitment into our hiring process, aiming to create an interview experience where you feel comfortable and confident showcasing your strengths. If there’s anything we can do to support that—big or small—please let us know.

• Define and execute a digital marketing strategy aligned to growth and revenue goals, with a particular focus on brand advertising and product marketing. • Design and lead targeted, integrated campaigns across the website (SEO/AIO), custom landing pages, video, email, social media, and paid media. • Own performance across the funnel, optimizing for conversion, efficiency, and pipeline impact. • Partner with product marketing to align on messaging and priorities. • Build targeted nurture programs that move prospects through the buyer journey. • Support segmentation, lead scoring, and lifecycle workflows to improve conversion. • Leverage analytics and dashboards to uncover insights and growth opportunities. • Drive campaign-specific social media strategies to amplify thought leadership. • Continuously evolve strategies using emerging trends, tools, and AI innovations.

• Lead the development of applied AI and GenAI capabilities across our products, including LLMs, agents, RAG, and AI-driven data solutions • Design, build, and operate end-to-end ML/AI pipelines—from experimentation to production, monitoring, and continuous improvement • Create scalable backend systems, observability, and evaluation frameworks that power GenAI across the company • Collaborate with AI, data, platform, and product teams to deliver AI solutions that meaningfully improve customer outcomes • Influence architecture and technical direction while mentoring engineers and raising the bar for engineering excellence • Drive innovation while balancing delivery, quality, and operational stability in a fast-moving environment

Title: Data Modeler Location: Pune, India Job Description: About Us Abacus Insights is transforming how data works for health plans. Our mission is simple: make healthcare data usable, so the people responsible for care and cost decisions can act faster, with confidence. We help health plans break down data silos to create a single, trusted data foundation. That foundation powers better decisions —so plans can improve outcomes, reduce waste, and deliver better experiences for members and providers alike. Backed by $100M from top investors, we’re tackling big challenges in an industry that’s ready for change. Our platform enables GenAI use cases by delivering clean, connected, and reliable healthcare data that can support automation, prioritization, and decision workflows—and it’s why we are leading the way. Our innovation begins with people. We are bold, curious, and collaborative—because the best ideas come from working together. Ready to make an impact? Join us and let's build the future together. About the Role We are looking for a Data Modeler to join a global data modeling team responsible for transforming business requirements into conceptual and logical data models. This role requires understanding of healthcare data domains (Claims, Membership, Enrollment, Provider, Billing, Interoperability - FHIR, HL7, ADT, CCDA, etc.) and experience in enterprise data modeling best practices. The Data Modeler will work closely with the Director of Data Modeling and Senior Data Modelers to adhere to data modeling standards, metadata governance, and follow data architecture strategies that align with business and technical needs. They will also collaborate with business analysts, product analysts, client implementation teams, and data governance teams to ensure models support operational and analytical use cases, high performance, and data integrity. This is an exciting opportunity for a data modeler with experience in Databricks, Snowflake, schema evolution, and modern data engineering practices to shape the future of healthcare data platforms. Your day to day Identify and define requirements for canonical data models as well as consumption use cases required by enterprise healthcare customers. Your activities will include: - Perform data profiling analysis. - Use SQL, data modeling and data integrity principles. - Build conceptual and logical data models aligned with enterprise architecture, business requirements and industry standards. - Follow data modeling best practices, including schema evolution, lossless data modeling, metadata management, and data versioning. - Use enterprise data modeling tools (Erwin, ER/Studio, DBT, or similar). - Ensure models support analytical (data science, actuarial, reporting) and operational (Claims, Care Management) use cases. - Maintain metadata, business glossary, entity relationship diagrams and data dictionaries to support data lineage and governance. - Partner with Client Management, Product, and Business Analyst teams to understand client needs, assess data model impact, and ensure data models and related artifacts meet or exceed expectations. - Work with client management teams to provide support and data model expertise during client implementation. What you bring to the team - 2+ years of experience in data modeling, enterprise data architecture, and cloud-based data engineering. - Expertise in enterprise data modeling tools (Erwin, ER/Studio, DBSchema, or similar). - NoSQL data model experience. - Strong hands-on experience with cloud data platforms (Databricks, Snowflake) and data lakehouse architectures. - Deep understanding of healthcare data models across Core Payer (Claims, Membership, Enrollment, Provider, Billing, Premium) and Clinical Interoperability (FHIR, HL7, ADT, CCDA, etc.) domains. - Experience with schema evolution, lossless data modeling, and data versioning strategies. - Strong background in SQL, data profiling, and metadata creation and maintenance. - Ability to learn from senior modelers, enforce modeling best practices, and drive adoption of enterprise data standards. - Excellent collaboration skills to work with data architects, engineers, and client implementation teams. What we would like to see, but not required - Healthcare reporting experience. - Experience with OBT, data lakehouse, and metadata management frameworks. - Familiarity with business intelligence (BI) and reporting solutions on modern cloud data warehouses. - Knowledge of APIs, SDKs, and data distribution frameworks for enabling real-time and batch data access. - Experience with data governance, security, and regulatory compliance in healthcare data management. What you’ll get in return - Competitive Leave & Benefits - Comprehensive health coverage - Equity for every employee – share in our success - Growth-focused environment – your development matters here Work arrangements - Standard hours: 8 hours/day, 5 days/week - Location: Pune, Hybrid (3 days a week in office) - Shift: 12pm-9pm IST Our Commitment as an Equal Opportunity Employer As a mission-led technology company helping to drive better healthcare outcomes, Abacus Insights believes that the best innovation and value we can bring to our customers comes from diverse ideas, thoughts, experiences, and perspectives. Therefore, we dedicate resources to building diverse teams and providing equal employment opportunities to all applicants. Abacus prohibits discrimination and harassment regarding race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. At the heart of who we are is a commitment to continuously and intentionally building an inclusive culture—one that empowers every team member across the globe to do their best work and bring their authentic selves. We carry that same commitment into our hiring process, aiming to create an interview experience where you feel comfortable and confident showcasing your strengths. If there’s anything we can do to support that—big or small—please let us know.

• Grow and expand a portfolio of existing health insurance customers by deeply understanding their priorities and mapping them to high-impact solutions across Abacus’s product suite. • Lead the full sales cycle from discovery to close—owning everything from initial outreach and executive presentations to RFIs, multi-day workshops, RFPs, negotiations, and deal signing. • Consistently deliver against a seven-figure quota across Abacus Insights’ most strategic payer customers. • Collaborate cross-functionally with Product, Marketing, Customer Success, Delivery, and Finance to craft compelling proposals, unlock expansion, and drive strong retention. • Bring a consultative, problem-solving approach—helping customers connect their challenges to clear value, building momentum from first conversation through signed growth initiatives.