{"id":549032,"url":"https://alion.io/job/magpie-health-analytics-data-engineer","title":"Data Engineer","company":{"id":449915,"name":"Magpie Health Analytics","domain":"magpiehealthanalytics.com","url":"https://alion.io/company/magpie-health-analytics","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Breezy","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Baltimore, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":90000,"max_usd":168000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":108},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Hadoop","optional":false},{"name":"Jenkins","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Amazon Aurora","optional":true},{"name":"Amazon S3","optional":true},{"name":"CloudFormation","optional":true},{"name":"dbt","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-07-27T19:36:54Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-10-28T19:14:19Z","board_verified":true,"closed_at":null,"days_open":93,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":92},"description":"Position Purpose:\nWe are seeking a Data Engineer to support the development of cloud-native data solutions that improve operational efficiency, support regulatory reporting, and drive actionable insight for our commercial and government healthcare clients. This role is responsible for building, maintaining, and optimizing scalable data pipelines and validation workflows across complex datasets-enabling downstream analytics, application logic, and system modernization initiatives.\nPrimary Duties & Responsibilities:\nDesign, develop, and maintain ETL/ELT pipelines using structured and semi-structured data from relational databases, flat files, APIs, and cloud data sources.\nCollaborate with backend and architecture teams to define data transformation flows aligned with dashboard and reporting application needs.\nDesign and optimize data schemas to ensure performance, integrity, and compatibility with reporting requirements.\nDevelop and maintain efficient, testable, and reusable data processing scripts using Python, SQL, and cloud-native tools.\nCollaborate with DevOps, analysts, and application developers to align pipelines with system architecture, storage strategy, and reporting needs.\n Implement data quality and validation checks and document data lineage and pipeline logic for audit and reuse.\nTroubleshoot performance issues in data jobs and support data pipeline operations across environments (DEV, VAL, PROD).\nContribute CI/CD workflows and automation strategies to promote rapid iteration and secure deployment of data services.\nAssist in developing or maintaining data documentation, including metadata, data dictionaries, and technical user guides.\nStay up to date with emerging technologies, techniques, and trends to inform product development and decision-making.\nMinimum Qualifications:\nBachelor's degree in computer science, engineering, statistics, or related field.\n4+ years of experience in a data engineering, data pipeline, or ETL/ELT development role.\nStrong proficiency in SQL, data transformation logic, and performance tuning for large datasets.\nProficiency with Python and libraries such as Pandas, PySpark, or Numpy.\nExperience with modern version control and CI/CD practices (e.g., GitHub Actions, Jenkins).\nUnderstanding of distributed computing solutions for data processing (e.g. AWS Glue, AWS EMR, Apache Hadoop, Apache Spark)\nExperience with data pipeline orchestration frameworks or serverless tools (e.g., AWS StepFunctions, AWS Glue Workflows, and/or AWS Lambda).\nPreferred Qualifications:\nExperience developing solutions in AWS cloud environments (S3, Lambda, Aurora, SNS, CloudFormation/CDK).\nExperience with Snowflake, Amazon RDS, or other cloud-native data warehouses.\nFamiliarity with modern data transformation tools (dbt, Dataform, or equivalent) and best practices for modular, tested SQL transformations within an ELT architecture.\nExperience supporting healthcare data systems or CMS data environments\nAWS certifications are a 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