{"id":1966629,"url":"https://alion.io/job/mpulse-data-engineer-ii","title":"Data Engineer II","company":{"id":2083092,"name":"mPulse","domain":"mpulse.com","url":"https://alion.io/company/mpulse-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Dayforce","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":94000,"max_usd":175000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":70},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Bitbucket","optional":false},{"name":"CI/CD","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Jenkins","optional":false},{"name":"Looker","optional":false},{"name":"MS SQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-10-06T05:00:00Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-10T00:27:02Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Job Summary:\nmPulse is seeking a highly motivated and detail-oriented Data Engineer II to join our Data Engineering team. In this role, you will design, develop, and maintain scalable data pipelines and platform capabilities that support our analytics, product, and AI/ML initiatives.\n\nOur data platform processes high-volume, high-velocity healthcare data, enabling insights and data-driven solutions for a growing client base. You will work cross-functionally with Data Operations, Product, Analytics, and Data Science teams to ensure data is reliable, performant, and aligned with business needs.\n\nThe ideal candidate brings strong experience in SQL, Python, dbt, and Airflow, along with a solid foundation in data warehousing and a passion for solving complex data challenges in a healthcare environment.\n\nDuties/Responsibilities:\nDesign, develop, and maintain scalable data pipelines (ETL/ELT) to support ingestion, transformation, and delivery of high-volume healthcare data.\nWrite, optimize, and maintain complex SQL queries for data transformation, validation, and performance tuning.\nDevelop and manage workflow orchestration using Apache Airflow, including DAG creation, monitoring, and troubleshooting.\nEnhance and scale data platform capabilities to support analytics, product features, and AI/ML use cases.\nBuild and maintain data quality frameworks, including automated data profiling, validation, and testing processes.\nMonitor and optimize pipeline performance, reliability, and efficiency in production environments.\nAnalyze complex data issues, identify root causes, and implement scalable solutions, clearly communicating findings to both technical and non-technical stakeholders.\nCollaborate with cross-functional teams (Data Operations, Product, Analytics, Data Science) to gather requirements and deliver high-quality data solutions.\nPartner with clinical and analytics teams to operationalize data-driven insights and reporting solutions.\nContribute to documentation of data pipelines, data models, and engineering processes to support maintainability and knowledge sharing.\n\nSkills/Abilities/Experience:\nStrong proficiency in SQL, including complex querying, data transformation, and performance optimization.\nProficiency in Python for data processing, automation, and integration tasks.\nExperience designing and building data pipelines (ETL/ELT) to support data ingestion, transformation, and delivery.\nHands-on experience with modern data warehousing platforms, such as Snowflake, PostgreSQL, Amazon Redshift, or Microsoft SQL Server.\nExperience with workflow orchestration tools, particularly Apache Airflow (DAG development, debugging, and maintenance).\nExperience using dbt (data build tool) to develop, test, and manage modular data transformation workflows.\nExperience working with cloud platforms, particularly AWS (e.g., S3, RDS, Lambda, Glue, DMS).\nExperience with version control systems and collaborative development workflows, such as GitHub or Bitbucket.\nFamiliarity with CI/CD practices and tools, such as Jenkins or GitHub Actions.\nExperience supporting data quality initiatives, including data profiling, validation, or monitoring frameworks.\nFamiliarity with data modeling and data warehousing concepts, including dimensional modeling.\nExposure to analytics, reporting, or data visualization tools (e.g., Tableau, Looker) is a plus.\nExperience working with healthcare data, including claims or clinical datasets, is a plus.\nFamiliarity with data science or machine learning workflows from a data engineering perspective is a plus.\n\nMinimum Qualifications:\nBachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.\n3+ years of professional experience in data engineering or a related field.\nStrong analytical and problem-solving skills, with the ability to work with complex datasets and identify root causes.\nStrong written and verbal communication skills, with the ability to effectively communicate technical concepts to both technical and non-technical stakeholders.\nAbility to collaborate effectively in cross-functional environments, working with engineering, product, analytics, and operations teams.\nStrong attention to detail and commitment to data accuracy, consistency, and quality.\nDemonstrated ability to manage multiple priorities and deliver high-quality work in a fast-paced environment.\nDemonstrates self-awareness, with the ability to recognize and communicate individual strengths and areas for growth.\nShows a strong willingness to learn, adapt, and support team members, contributing to a collaborative and positive team environment.\nEffectively communicates progress, priorities, and status updates to both internal and external stakeholders in a clear and timely manner.\n\nPhysical Requirements:\nAbility to stand and sit for extended period of time.\nAbility to lift 50 lbs. weight.","description_format":"text","description_chars":4916,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United 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