Senior Data Engineer

Senior Data Engineerat Zócalo Health Remote (Full Time) Compensation: $160,000 - $180,000 (per year)About UsZócalo Health is a tech-enabled, community-oriented primary care organization serving people who have historically been underserved by the one-size-fits-all healthcare system. We partner with health plans, providers, and community organizations to deliver culturally competent primary care, behavioral health, and social care.Our model is built for populations with high medical and social complexity, where fragmented care drives poor outcomes and unnecessary cost. We combine local, community-based teams with virtual care and modern technology to deliver coordinated, whole-person care where members live and receive support.Founded in 2021, Zócalo Health is backed by leading healthcare and mission-aligned investors and is scaling rapidly across states and populations. We are building a durable care platform designed to perform in constrained healthcare environments and to lead the shift toward accountable, value-based care.Role DescriptionThe Senior Data Engineer will join Zócalo Health as we build the data platform that powers analytics, product measurement, and operational visibility across the company. This is a hands-on building role at a foundational stage: you will design and ship the pipelines, ingestion frameworks, and data models that the rest of the company depends on.The primary focus of this role is establishing a scalable, durable data platform. This includes laying the groundwork for longer-term initiatives such as the longitudinal patient record, population-level analytics, and product instrumentation. You will partner closely with Engineering and Product to ensure the data platform supports roadmap priorities and outcome measurement as the company grows.This position reports to the Principal Data Engineer and partners closely with Engineering and Product.In your first 12 months, you will:Build and operate production-grade ingestion pipelines from core clinical, operational, and third-party systems into our Databricks lakehouseDevelop and maintain dbt models that turn raw data into clean, well-documented, analytics-ready datasetsEstablish data quality, testing, and monitoring practices that make pipelines reliable and trustworthyHelp shape ingestion patterns and architecture standards alongside the Principal Data EngineerEnable company-wide metrics for care outcomes and operationsCollaborate with cross-functional leads to develop and iterate on a suite of core operational dashboards, ensuring teams have the self-service tools they need to track company metrics and outcomes.The Senior Data Engineer will contribute in the following ways:Design, build, and operate production data pipelines across clinical, operational, and third-party systems using API-based ingestion, Change Data Capture (CDC), and event- or webhook-driven patternsBuild and maintain transformation layers in dbt, including tests, documentation, and reusable modelsDevelop and refine core analytical and longitudinal data models used across the companyImplement testing, monitoring, and observability to ensure data quality, pipeline reliability, and system performanceApply strong engineering fundamentals to improve the scalability, performance, and cost-efficiency of data systems on AWS and DatabricksPartner with Product to support metric definitions, outcome measurement, and reporting needsContribute to engineering standards, code review, and a culture of knowledge sharing and continuous improvementPartner with business, product, and engineering stakeholders to design and build intuitive data visualizations and dashboards that drive actionable insights and program visibility.Core Technologies (current and planned)Cloud: AWSLakehouse / data platform: DatabricksTransformations: dbtLanguages: SQL and Python (primary languages for ingestion and transformation)Ingestion patterns: API-based ingestion, Change Data Capture (CDC), and event- or webhook-driven pipelines, including frameworks such as PySpark and Spark Structured Streaming on DatabricksOrchestration: workflow orchestration (e.g., Databricks Workflows or Airflow)Qualifications5+ years of experience in data or backend engineering roles with significant data platform responsibilityHands-on experience building and operating production-grade data pipelines and ingestion frameworksStrong proficiency in SQL and Python for data ingestion, processing, and transformationExperience with a cloud data platform; experience with AWS and Databricks (or a comparable Spark-based lakehouse) strongly preferredExperience building SQL-based transformation workflows; hands-on experience with dbt preferredStrong computer science fundamentals, including comfort reasoning about distributed systems and data processing at scaleAbility to diagnose and resolve performance, reliability, and data quality issues in complex systemsStrong ownership mindset and comfort operating in ambiguous, fast-growing envi