Data Engineer

Alpharetta, GA - USA

Monthly Salary: Not Disclosed

Posted on: 2 hours ago

Vacancies: 1 Vacancy

Job Summary

Title: Data Engineer
Location: Alpharetta GA or Remote

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.
Skillset:
10 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).
Bonus: Prior experience working on complex migration projects

Best Regards:

Bindu M
Phone:
Email:

Title: Data Engineer Location: Alpharetta GA or Remote 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 partne...

Title: Data Engineer
Location: Alpharetta GA or Remote

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.
Skillset:
10 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).
Bonus: Prior experience working on complex migration projects