{"id":182171,"url":"https://alion.io/job/sedgwick-data-engineer","title":"Data Engineer","company":{"id":5707,"name":"Sedgwick","domain":"sedgwick.com","url":"https://alion.io/company/sedgwick","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":86,"open_postings":48,"ghost_share":0,"stale_share":0.708,"repost_share":0.063,"time_to_fill_p50_days":20,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","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":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":88000,"max_usd":159000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":340},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure SQL Database","optional":false},{"name":"Db2","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Feature Store","optional":false},{"name":"IAM","optional":false},{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-08-31T00:00:00Z","employer_posted_date":"2026-08-31","last_verified_at":"2026-10-17T15:07:50Z","board_verified":true,"closed_at":null,"days_open":47,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":47},"description":"By joining Sedgwick, you'll be part of something truly meaningful. It’s what our 33,000 colleagues do every day for people around the world who are facing the unexpected. We invite you to grow your career with us, experience our caring culture, and enjoy work-life balance. Here, there’s no limit to what you can achieve.\nNewsweek Recognizes Sedgwick as America’s Greatest Workplaces National Top Companies\nCertified as a Great Place to Work®\nFortune Best Workplaces in Financial Services & Insurance\nData EngineerPRIMARY PURPOSE: The Gen AI Engineer within the Transformation Office serves as the hands-on architect of the enterprise data supply chain for the organization’s most advanced analytics, data science, and AI initiatives. This role performs the critical engineering work required to deliver high-fidelity, production-grade data that powers machine learning models, feature stores, and generative AI applications.\nOperating as a “day-one” builder, the Gen AI Engineer designs and delivers data pipelines that bridge legacy on-premise systems-including mainframes, SQL Server, and DB2-with modern cloud platforms such as Snowflake and AWS/Azure AI ecosystems. The role ensures that data is not merely transferred, but deliberately engineered to meet the statistical, performance, and governance requirements of model training, inference, and RAG-based AI systems.\nESSENTIAL FUNCTIONS AND RESPONSIBILITIES\nDesigns, builds, and maintains resilient ETL/ELT pipelines that ingest data from on-premise systems, AWS services (S3, RDS), and Azure platforms (Blob Storage, Azure SQL), centralizing and curating data for consumption in Snowflake and downstream AI services.\nDevelops and maintains feature stores and analytically optimized datasets that support machine learning workflows, ensuring data is clean, versioned, reproducible, and statistically valid for Data Science teams.\nEngineers data pipelines that enable generative AI use cases, including the automated extraction, transformation, chunking, and loading of structured and unstructured data into vector databases across AWS and Azure environments.\nActs as a Snowflake power user and technical lead, implementing advanced data modeling patterns, Snowpipe automation, and compute and storage optimization to support high-concurrency analytics and AI workloads.\nExecutes non-invasive data extraction strategies to unlock mission-critical data from decades-old legacy systems while preserving system stability and avoiding disruption to core business operations.\nDesigns and manages complex, cross-platform data workflows using orchestration tools such as Airflow, AWS Step Functions, and Azure Data Factory to ensure reliable, synchronized data movement across the organization’s multi-cloud architecture.\nPartners closely with central IT, database administrators, infrastructure, and security teams to resolve connectivity and access challenges-including PrivateLink, IAM, network segmentation, and firewall controls-while securing production approval for new data integrations.\nImplements automated data quality, validation, and observability frameworks to detect data drift, anomalies, and integrity issues that could negatively impact production analytics, machine learning, or AI systems.\nDrives efficiency across the data ecosystem by optimizing storage, compute usage, and query performance in Snowflake, AWS, and Azure, ensuring responsible cost management and measurable ROI for Transformation Office initiatives.\nOperates as a dedicated engineering partner to MLOps, Data Science, and AI teams, rapidly iterating on evolving data requirements and translating experimental use cases into scalable, production-ready data solutions.\nADDITIONAL FUNCTIONS and RESPONSIBILITIES\nPerforms other duties as assigned.\nTravel as required.\nQUALIFICATIONS\nEducation & Licensing\nMaster’s degree in Computer Science, Data Engineering, or a related field from an accredited college or university preferred.\nExperience\nSix (6)years of hands-on data engineering experience, with a track record of building production-grade pipelines for Data Science and AI in multi-cloud environments or equivalent combination of education and experience required.\nSkills & Knowledge\nExpert-level proficiency in Snowflake architecture, including data sharing, performance tuning, and the integration of Snowflake with external cloud AI services\nAdvanced, hands-on knowledge of AWS (S3, Glue, Lambda) and Azure (Data Factory, Synapse) data services\nMastery of Python, SQL, and PySpark. Deep experience with data orchestration and containerization (Docker)\nProven ability to interface with \"old world\" tech (on-premise SQL, Mainframe extracts, flat files) and transform it for modern cloud consumption\nA strong understanding of the specific data needs for Machine Learning (feature engineering) and Generative AI (vectorization and embedding pipelines)\nA \"get-it-done\" attitude, capable of navigating enterprise bureaucracy and technical debt to ship code at the speed required by a Transformation Office\nAbility to work in a team environment\nAbility to meet or exceed Performance Competencies\nWORK ENVIRONMENT\nWhen applicable and appropriate, consideration will be given to reasonable accommodations.\nMental: Clear and conceptual thinking ability; excellent judgment, troubleshooting, problem solving, analysis, and discretion; ability to handle work-related stress; ability to handle multiple priorities simultaneously; and ability to meet deadlines\nPhysical: Computer keyboarding, travel as required\nAuditory/Visual : Hearing, vision and talking\nThe statements contained in this document are intended to describe the general nature and level of work being performed by a colleague assigned to this description. They are not intended to constitute a comprehensive list of functions, duties, or local variances. Management retains the discretion to add or to change the duties of the position at any time.\nSedgwick is an Equal Opportunity Employer and a Drug-Free Workplace.\nIf you're excited about this role but your experience doesn't align perfectly with every qualification in the job description, consider applying for it anyway! Sedgwick is building a diverse, equitable, and inclusive workplace and recognizes that each person possesses a unique combination of skills, knowledge, and experience. 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