• Querying complex source systems in a range of health data sources (e.g., EMRs, ECG data, DICOM data) to identify and map data elements in order to create high-quality datasets to support training AI algorithms and conducting in-depth analyses of patient journeys • Developing and maintaining data transformation pipelines using dbt and Snowflake, ensuring data quality, lineage, and transparency. • Harmonizing multimodal data from multiple health systems into a unified ontological layer ready for use by data scientists and AI developers, with input from clinical experts across the company. • Performing complex data extraction, manipulation, and summarization of large databases to create analytical data models; • Implementing data quality tests, CI/CD and version control best practices for analytics codebases throughout the data modeling pipeline. • Supporting software engineers in optimizing processes and technical solutions for de-identification and ETL of clinical data from disparate health system cloud environments to Dandelion Health’s data warehouse. • Working with experts in natural language processing (NLP) to structure and model discrete clinical concepts data that have been abstracted from unstructured and semi-structured text data. • Collaborating with technical and clinical subject matter experts and Dandelion customers to translate research and model requirements into engineering solutions.

• Own implementation and on-going operation of recurring analytics pipelines (e.g., Airflow DAGs) including monitoring, alerting, and reliability improvements • Lead architectural evolution of the analytics platform, including schema standardization, DAG consolidation, and modernization of legacy workflows • Drive cross-team technical alignment when consolidating duplicated or inconsistent analytics outputs • Build and maintain base analytics tables and metrics with strong schema discipline and reproducible computation • Define and implement reliability standards (SLOs, observability patterns, runbooks) adopted across analytics pipelines • Improve transparency and usability through documentation, discoverability, and clear data contracts • Optimize distributed compute and SQL query performance; design data layouts (partitioning, file sizing) for columnar storage • Mentor engineers through design reviews and raise the bar for operational and modeling rigor

• Own the analytics platform roadmap — prioritizing improvements, architecture decisions, and tooling enhancements in partnership with IT and the Head of Data, Analytics and AI Solutions • Define and enforce platform SLAs, data quality standards, and operational runbooks • Serve as technical mentor to the Analytics Engineer/Data Scientist • Own the end-to-end data pipeline from ingestion through transformation and delivery into BI tools and data products across our AWS stack (Step Functions, Glue, Snowflake, dbt, Sigma) • Partner with third-party vendors to maintain DataOps infrastructure using infrastructure-as-code practices • Monitor pipeline health and drive timely resolution of data issues with providers and consumers • Maintain and evolve an enterprise data model, data dictionary, and business rules agnostic across sources, insurance products, and distributors • Enforce Snowflake security best practices — masking policies, RBAC, and least-privilege access — aligned to GLBA NPI/NPI-H classification requirements • Champion data governance, lineage, and quality standards; support NAIC MDL-668 audit trails, SOC 2 Type II evidence collection, and adherence to CCPA/CPRA, HIPAA, and state breach notification requirements • Design and maintain systems of record for core insurance domains (policy, claims, billing, distribution, customer) with proper versioning, effective dating, and audit history • Own database design and management for AI tools and models, including vector databases, feature stores, and model input/output pipelines • Design and maintain a semantic layer enabling natural language insight discovery • Own executive and operational dashboards from board-level reporting down to day-to-day operations • Set BI development standards for the Analytics Engineer/Data Scientist to execute against • Maintain and enhance actuarial data pipelines including distributed compute and Glue Jobs for data curation • Advise on data architecture decisions supporting future AI/ML and modeling use cases

• Designing and implementing graph algorithms that analyze large cryptocurrency transaction networks at multi-blockchain scale • Researching new graph-native technology to evaluate benefit to data science and data engineering teams at TRM • Working on a highly cross-functional team that collaborates with cryptocurrency investigators to identify key user stories and requirements for new graph algorithms and features • Understanding and refining TRM’s risk models which analyze large networks of cryptocurrency transactions to assign risk scores to addresses • Communicating complex implementation details to a variety of audiences from investigators and customer success stakeholders to data engineers and data scientists • Integrating with a diverse set of data inputs ranging from raw blockchain data to complex model outputs