Machinify, Inc.
Machinify is the leading provider of AI-powered software products that transform healthcare claims and payment operations. Each year, the healthcare industry generates over $200B in claims mispayments, creating incredible waste, friction and frustration for all participants: patients, providers, and especially payers. Machinify's revolutionary AI-platform has enabled the company to develop and deploy, at light speed, industry-specific products that increase the speed and accuracy of claims processing by orders of magnitude.
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.
What You'll Do
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. What You Bring
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). Why Join Us
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.
We're hiring across multiple levels for this role. Final level and title will be determined based on experience and performance during the interview process.
Equal Employment Opportunity at Machinify
Machinify is committed to hiring talented and qualified individuals with diverse backgrounds for all of its positions. Machinify believes that the gathering and celebration of unique backgrounds, qualities, and cultures enriches the workplace.
See our Candidate Privacy Notice at: https://www.machinify.com/candidate-privacy-notice/
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.
What You'll Do
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. What You Bring
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). Why Join Us
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.
We're hiring across multiple levels for this role. Final level and title will be determined based on experience and performance during the interview process.
Equal Employment Opportunity at Machinify
Machinify is committed to hiring talented and qualified individuals with diverse backgrounds for all of its positions. Machinify believes that the gathering and celebration of unique backgrounds, qualities, and cultures enriches the workplace.
See our Candidate Privacy Notice at: https://www.machinify.com/candidate-privacy-notice/