[Hiring] Lead Data Engineer @Ilant Health

Role Description

At Ilant Health, data is the cornerstone of our mission. It drives our clinical precision, shapes our business strategy, and provides the measurable ROI necessary to expand access for employers and health plans. We are looking for a Lead Data Engineer to architect the "source of truth" that powers our value-based care models for obesity and cardiometabolic health.

In this role, you will not just build pipelines; you will be the architect of our data platform. You will own the ingestion of complex healthcare datasets (claims, eligibility, clinical labs), the design of our "Single Patient View," and the creation of next-generation internal tools that allow non-technical stakeholders to query our data using natural language.

Key Responsibilities

• Data Architecture and Strategy (The “Blueprint”)
  • Design the "Single Patient View": Architect a unified data model that stitches together fragmented data sources (e.g., linking a pharmacy claim for Wegovy, a clinical lab result for HbA1c, and user engagement metrics from the Ilant app into a cohesive longitudinal record).
  • Scalability Planning: Design a cloud-native infrastructure (likely Snowflake/AWS) capable of handling 100x Member growth without requiring a total refactor.
  • Buy vs. Build Decisions: Evaluate and select the right tooling for ingestion (e.g., Fivetran vs. custom Python) and orchestration (e.g., Airflow vs. Dagster) to maintain low engineering overhead while maximizing output.
  • Conversational Intelligence Layer (GenAI/LLM): Architect and implement a "Text-to-Data" interface (leveraging LLMs/RAG) that allows business decision-makers to interact with our data via prompts (e.g., similar to Gemini/ChatGPT).
• Pipeline Engineering (The “Plumbing”)
  • Data Consumption Layer: Ensure the reliability and low-latency availability of the data assets (dbt models, feature stores) consumed by the Data Science and Analytics teams, guaranteeing they always have fresh, trustworthy data for modeling and reporting.
  • External Data Integration (Primary Mandate): Own the end-to-end reliability of mission-critical external files. You are responsible for the system that ingests, validates, and standardizes these files from payers/employers.
  • Claims Ingestion Engine: Build robust, fault-tolerant pipelines to handle the notoriously messy formats of payer data (EDI 837/835, raw CSVs, JSON) and standardize them into a clean, queryable schema.
  • dbt Model Ownership: Oversee the transformation layer (using dbt), creating a "Gold" layer of data that is business-ready for analysts, product features, and the conversational AI layer.
• Data Quality and Trust (The “Guardrails”)
  • Pipeline Reliability & Operational Uptime: You own the "uptime" of our data platform. Ensure all scheduled ingestion and transformation jobs run successfully and on time. You are the first line of defense when a pipeline fails, leading the root cause analysis (RCA) and resolution to minimize downtime.
  • Automated Testing & Observability: Implement "Data Observability" tools (e.g., Great Expectations, Monte Carlo, or custom equivalents) to catch issues before they hit the dashboard (e.g., Configure alerts to trigger if an eligibility file arrives with 50% fewer records than the previous month).
  • Governance & Compliance: Act as the technical custodian of HIPAA compliance. Ensure all PII/PHI is encrypted, masked, and accessed only via strict Role-Based Access Controls (RBAC).
  • Master Data Management (MDM): Implement identity resolution logic to handle conflicts across sources (e.g., ensuring "Jane Doe" in a Cigna claims file is correctly matched to "Jane Doe" in the Ilant app database).
• Leadership and Collaboration
  • Partner with Product: Work directly with the CPO and Product Managers to assess the technical feasibility of new features (e.g., "Can we accurately calculate 'time to goal weight' given the current data latency?").
  • Partner with Data Science: Collaborate to productionize predictive models (e.g., patient risk stratification, weight loss trajectory). You will build the MLOps infrastructure that takes a model from a Jupyter notebook to a scalable, real-time inference API within our product.

Qualifications

• Experience: 7+ years in Data Engineering, with at least 3+ years in a Lead or Architectural role.
• Strategic Maturity: Demonstrated ability to make high-stakes "Buy vs. Build" decisions and architect systems for 10x scale, prioritizing long-term stability and maintainability over short-term patches.
• Healthcare Native: Deep familiarity with healthcare data standards (HL7, FHIR, ICD-10, CPT, NDC) and the specific challenges of claims/eligibility ingestion.
• GenAI/LLM Interest: Practical experience or strong interest in building semantic layers for LLM applications (RAG, Vector DBs, or prompt engineering for analytics).

Requirements

• Languages: Python (Advanced), SQL (Expert).
• Cloud: AWS.
• Warehousing: Snowflake, BigQuery, or Databricks.
• Transformation: dbt (Data Build Tool).
• Orchestration: Airflow, Dagster, or Prefect.

Benefits

• Fully remote environment – work from anywhere while maintaining meaningful collaboration with a distributed team.
• Comprehensive health benefits – medical, dental, and vision coverage to support you and your family.
• Paid time off – 2 weeks of PTO to rest, recharge, and take the time you need.
• Flexible floating holiday – one additional day each year to celebrate what matters most to you.
• Paid sick leave – 5 sick days so you can prioritize your health when needed.
• 11 paid company holidays throughout the year.
• 401(k) retirement plan to help you invest in your future.
• Healthcare and Dependent Care FSA options for additional tax-advantaged savings.