Senior Data Engineer, Machinify
Infra
+ Equity
AI cost reduction platform for healthcare
Job no longer available
AI cost reduction platform for healthcare
501-1000 employees
Job no longer available
+ Equity
501-1000 employees
Company mission
To develop software that helps people get the right medical care, at the right time, at the right price.
Role
Who you are
- If you enjoy owning end-to-end workflows, shaping data standards, and driving impact in a fast-moving environment, this is your opportunity
- 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
- Bonus: Familiarity with healthcare data (837, 835, EHR, UB04, claims normalization)
What the job involves
- 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
- 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
Our take
Founded in 2016, Machinify's original aims were to help re-tool business operations across industries, transform data into decisions, and enable established enterprises to keep up with the rapidly digitizing space. Nowadays, the company has pivoted to employ its AI expertise specifically in the healthcare industry, where it has already found success with Payers and secured several market-leader customers.
Healthcare is one of many industries where companies have vast pools of data but struggle to make the most of it due to operational restraints. Machinify makes it easier for Payers to implement AI-powered decision-making, applying it to the authorization of treatments, review of medical claims and bills, and diagnosis of underlying conditions.
With intelligent applications seeing broader adoption in the healthcare market, the company will likely see more funding and growth in the coming years. Its acquisitions of companies such as Performant Healthcare will enable it to easily expand its AI-powered operating system, creating an even more robust offering for clients.
Steph
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 3 rounds)
Oct 2018
$10m
SERIES A
Mar 2016
$2.8m
SEED
Total funding: $15.6m
Company benefits
- Work from anywhere in the US
- Home office stipend
- Work on big projects that impact millions of lives
- Premium medical, dental & vision benefits
- 401k plan sponsorship
- No-meeting day every week
- Individual Equity and Tax Advisory services
- Flexible PTO
- Home office stipend
- Mentorship from industry leaders
- Annual learning and development stipend
Company HQ
University South, Palo Alto, CA
Leadership
Prasanna Ganesan
(Co-Founder, CTO & Board Member)Previously co-founded VUDU where they served as the CTO and General Manager. They also served as the CTO at Klip before founding Machinify.
David Pierre
(CEO)20 years of experience leading and scaling high-growth healthcare technology companies. Is also CEO at Rawlings, Apixio PI and VARIS.
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