Senior Data Engineer

Role Description

The 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 lakehouse
• Develop and maintain dbt models that turn raw data into clean, well-documented, analytics-ready datasets
• Establish data quality, testing, and monitoring practices that make pipelines reliable and trustworthy
• Help shape ingestion patterns and architecture standards alongside the Principal Data Engineer
• Enable company-wide metrics for care outcomes and operations
• Collaborate 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 patterns
• Build and maintain transformation layers in dbt, including tests, documentation, and reusable models
• Develop and refine core analytical and longitudinal data models used across the company
• Implement testing, monitoring, and observability to ensure data quality, pipeline reliability, and system performance
• Apply strong engineering fundamentals to improve the scalability, performance, and cost-efficiency of data systems on AWS and Databricks
• Partner with Product to support metric definitions, outcome measurement, and reporting needs
• Contribute to engineering standards, code review, and a culture of knowledge sharing and continuous improvement
• Partner 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: AWS
• Lakehouse / data platform: Databricks
• Transformations: dbt
• Languages: 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 Databricks
• Orchestration: workflow orchestration (e.g., Databricks Workflows or Airflow)

Qualifications

• 5+ years of experience in data or backend engineering roles with significant data platform responsibility
• Hands-on experience building and operating production-grade data pipelines and ingestion frameworks
• Strong proficiency in SQL and Python for data ingestion, processing, and transformation
• Experience with a cloud data platform; experience with AWS and Databricks (or a comparable Spark-based lakehouse) strongly preferred
• Experience building SQL-based transformation workflows; hands-on experience with dbt preferred
• Strong computer science fundamentals, including comfort reasoning about distributed systems and data processing at scale
• Ability to diagnose and resolve performance, reliability, and data quality issues in complex systems
• Strong ownership mindset and comfort operating in ambiguous, fast-growing environments
• Clear communicator able to partner effectively with technical and non-technical stakeholders
• Experience building dashboards or analytical outputs used by executives and frontline teams

Preferred Qualifications

• Experience working with healthcare, care delivery, or other regulated data environments
• Familiarity with HIPAA requirements and handling of sensitive health or customer data
• Experience building streaming data pipelines or event-driven architectures
• Experience implementing data observability, lineage, or quality monitoring tools
• Familiarity with AI-assisted development tools and automation in engineering workflows
• Early-stage startup experience strongly preferred

Benefits

• Equity compensation package
• Comprehensive benefits including medical, dental, and vision
• 401k
• Flexible PTO policy - take the time you need to recharge
• $1,000 home office stipend
• We provide the equipment needed for this role.
• Opportunity for rapid career progression with plenty of room for personal growth.