Sr. Data Engineer | Analytics

Machinify•Palo Alto, CA

21h

About The Position

Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Deployed by over 60 health plans, including many of the top 20, and representing more than 160 million lives, Machinify brings together a fully configurable and content-rich, AI-powered platform along with best-in-class expertise. We’re constantly reimagining what’s possible in our industry, creating disruptively simple, powerfully clear ways to maximize financial outcomes and drive down healthcare costs. Why This Role Matters As a Data Engineer, you’ll be at the heart of transforming raw external data into powerful, trusted datasets that drive payment, product, and operational decisions. You’ll work closely with product managers, data scientists, subject matter experts, engineers, and customer teams to build, scale, and refine production pipelines — ensuring data is accurate, observable, and actionable. You’ll also play a critical role in onboarding new customers, integrating their raw data into our internal models. Your pipelines will directly power the company’s ML models, dashboards, and core product experiences. If you enjoy owning end-to-end workflows, shaping data standards, and driving impact in a fast-moving environment, this is your opportunity.

Requirements

4+ years of experience as a Data Engineer (or equivalent), building production-grade pipelines.
Strong expertise in Python, Spark SQL, and Airflow.
Experience processing large-scale file-based datasets (CSV, Parquet, JSON, etc) in production environments.
Experience mapping and standardizing raw external data into canonical models.
Familiarity with AWS (or any cloud), including file storage and distributed compute concepts.
Experience onboarding new customers and integrating external customer data with non-standard formats.
Ability to work across teams, manage priorities, and own complex data workflows with minimal supervision.
Strong written and verbal communication skills — able to explain technical concepts to non-engineering partners.
Comfortable designing pipelines from scratch and improving existing pipelines.
Experience working with large-scale or messy datasets (healthcare, financial, logs, etc.).
Experience building or willingness to learn streaming pipelines using tools such as Kafka or SQS.

Nice To Haves

Familiarity with healthcare data (837, 835, EHR, UB04, claims normalization).

Responsibilities

Design and implement robust, production-grade pipelines using Python, Spark SQL, and Airflow to process high-volume file-based datasets (CSV, Parquet, JSON).
Lead efforts to canonicalize raw healthcare data (837 claims, EHR, partner data, flat files) into internal models.
Own the full lifecycle of core pipelines — from file ingestion to validated, queryable datasets — ensuring high reliability and performance.
Onboard new customers by integrating their raw data into internal pipelines and canonical models; collaborate with SMEs, Account Managers, and Product to ensure successful implementation and troubleshooting.
Build resilient, idempotent transformation logic with data quality checks, validation layers, and observability.
Refactor and scale existing pipelines to meet growing data and business needs.
Tune Spark jobs and optimize distributed processing performance.
Implement schema enforcement and versioning aligned with internal data standards.
Collaborate deeply with Data Analysts, Data Scientists, Product Managers, Engineering, Platform, SMEs, and AMs to ensure pipelines meet evolving business needs.
Monitor pipeline health, participate in on-call rotations, and proactively debug and resolve production data flow issues.
Contribute to the evolution of our data platform — driving toward mature patterns in observability, testing, and automation.
Build and enhance streaming pipelines (Kafka, SQS, or similar) where needed to support near-real-time data needs.
Help develop and champion internal best practices around pipeline development and data modeling.

Benefits

Real impact — your pipelines will directly support decision-making and claims payment outcomes from day one.
High visibility — partner with ML, Product, Analytics, Platform, Operations, and Customer teams on critical data initiatives.
Total ownership — you’ll drive the lifecycle of core datasets powering our platform.
Customer-facing impact — you will directly contribute to successful customer onboarding and data integration.

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