Staff Data Engineer

Synthesis Health

Who We Are

We’re a mission- and values-driven company with tremendous dedication to our customers. Our 100% remote team is dedicated to a common goal – to revolutionize healthcare through innovation, collaboration, and commitment to our core values and behaviors. About the Opportunity We are looking for a Staff Data Engineer who serves as the universal data architect for our platform.

In this role, you will bridge the gap between application engineering and data infrastructure. You will possess a "big picture" understanding of our entire data ecosystem—OLTP, OLAP, NoSQL, and Relational. You will own the optimization of our high-volume data pipelines, but equally, you will tune the operational databases that power our services and design the warehousing strategies that drive our analytics.

You will be the authority on data physics: defining how we ingest massive bursts of medical information, how we model it for high-performance transactional locking, and how we transform it for analytical querying without friction. This is a high-leverage leadership role. You will set the standard for data engineering excellence, mentoring Senior engineers and defining the architectural patterns that keep our platform performant as we scale 100x and beyond.

Key Responsibilities

Polyglot Data Architecture Performance Universal Store Optimization: You will be the ultimate authority on the performance of our persistent stores. You will tune AlloyDB (PostgreSQL) for complex transactions (OLTP), optimize NoSQL layers, and structure BigQuery for analytical speed (OLAP). Prevent Congestion Latency: You will proactively identify and resolve "hot spots" in our data architecture.

You will design strategies for sharding, partitioning, and active-archiving that ensure our operational systems remain lean while our analytical history grows indefinitely. High-Volume Pipeline Ingestion Strategy Ingestion at Scale: You will architect robust, backpressure-aware pipelines to handle high-throughput ingestion of HL7 and DICOM metadata. You will ensure our systems can absorb bursty traffic without degrading the user experience.

Stream Batch Convergence: You will design the flows that move data from operational stores to our data warehouse, ensuring freshness and consistency. You will determine when to use real-time streams versus batch processing to balance cost and latency. Service Design Data Governance Define Data Contracts: You will act as a key voice on the Architecture Review Board (ARB), defining the "rules of the road" for how microservices interact with the data layer.

You will enforce strict data contracts that decouple services from the underlying storage engine. Mentorship: You will elevate the data engineering capabilities of the entire organization. You will mentor other engineers on advanced query optimization, data modeling (Star Schema vs.

3NF), and the nuances of distributed data consistency.

What We’Re Looking For

Elite Data Engineering Experience: 8+ years of experience designing and optimizing data-intensive applications. You have a track record of solving hard scaling problems across the entire data lifecycle. Universal Data Fluency: You have deep expertise across the spectrum: Relational (AlloyDB/PostgreSQL internals, MVCC, locking), NoSQL (Document stores, Key-Value), and Warehousing (BigQuery/Snowflake internals, columnar storage).

Pipeline Proficiency: Expert-level knowledge of building and optimizing data pipelines using tools like Kafka/PubSub, Beam/Dataflow, or dbt. You understand how to handle late data, duplicates, and exactly-once processing. Service System Design: You understand how data stores fit into a broader microservices architecture.

You can design synchronous vs. asynchronous data access patterns that protect the database from application-layer thundering herds. Coding Skills: Strong proficiency in Python, Java, SQL.

You write clean, maintainable code for infrastructure and data transformation.