• Criar e evoluir **módulos Terraform** para padronizar componentes da plataforma de dados (redes, IAM, storage, computação, pipelines etc.). • Arquitetar, desenvolver e manter **pipelines e processos ETL/ELT**, garantindo qualidade, escalabilidade e observabilidade. • Estabelecer padrões e boas práticas de **DataOps**, incluindo versionamento, testes, deployments e monitoramento. • Dar suporte técnico e consultivo aos times de dados, ajudando a estruturar soluções robustas e alinhadas à arquitetura corporativa. • Implementar mecanismos de **governança**, segurança e otimização de custos em ambientes de dados na nuvem.

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. Newsweek Recognizes Sedgwick as America’s Greatest Workplaces National Top Companies Certified as a Great Place to Work® Fortune Best Workplaces in Financial Services & Insurance Senior Data Engineer- Data Science & AI Role Overview As a Senior Data Engineer within the Transformation Office, you are the hands-on architect of the data supply chain for our most advanced initiatives. You will be responsible for the "heavy lifting" required to fuel Data Science models and AI applications with high-fidelity data. Your mission is to build the pipelines that bridge our legacy on-prem systems (Mainframes, SQL Server, DB2) with our modern Snowflake environment and AWS/Azure AI stacks. You are a "day-one" builder who ensures that data is not just moved, but engineered for the specific requirements of model training, feature stores, and RAG-based AI systems. Key Responsibilities • Hybrid Data Pipeline Execution: Design and implement robust ETL/ELT pipelines to ingest data from legacy on-prem sources, AWS (S3/RDS), and Azure (Blob/SQL), centralizing it for consumption in Snowflake and AI services. • Engineering for Data Science: Build and maintain Feature Stores and specialized datasets optimized for machine learning, ensuring Data Scientists have immediate access to clean, versioned, and statistically valid data. • Engineering for AI (RAG & LLMs): Develop the data pipelines required for Generative AI, including the automated extraction, chunking, and loading of unstructured data into vector stores across AWS and Azure. • Snowflake Power-User Execution: Act as the technical lead for our Snowflake data warehouse, implementing sophisticated data modeling, Snowpipe automation, and compute optimization to support high-concurrency AI workloads. • Legacy "Back-Reach" Engineering: Execute non-invasive data extraction patterns to unlock mission-critical data from decades-old on-premise systems without disrupting core business operations. • Multi-Cloud Orchestration: Manage complex, cross-platform data workflows using Airflow, Step Functions, or Azure Data Factory, ensuring the synchronization of data across our multi-cloud AI posture. • IT & Security Diplomacy: Partner directly with central IT, Database Administrators, and Security teams to solve connectivity hurdles (PrivateLink, IAM, firewalls) and secure "license to operate" for new data flows. • Data Quality for Model Integrity: Implement automated validation and observability layers to detect data drift and quality issues that could compromise the accuracy of production AI and Data Science models. • Cost & Performance Management: Drive the efficiency of our data stack by optimizing storage and query performance in Snowflake, AWS, and Azure to manage the ROI of the Transformation Office. • Direct Stakeholder Collaboration: Work as a dedicated engineering partner to MLOps and Data Science teams to rapidly iterate on data requirements for evolving AI use cases. Qualifications • Education: Bachelor’s degree in Computer Science, Data Engineering, or a related field is required. A Master’s degree is highly desirable. • Proven Execution: 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. • Snowflake Mastery: Expert-level proficiency in Snowflake architecture, including data sharing, performance tuning, and the integration of Snowflake with external cloud AI services. • Multi-Cloud Proficiency: Advanced, hands-on knowledge of AWS (S3, Glue, Lambda) and Azure (Data Factory, Synapse) data services. • Technical Stack: Mastery of Python, SQL, and PySpark. Deep experience with data orchestration and containerization (Docker). • Legacy Expertise: Proven ability to interface with "old world" tech (on-premise SQL, Mainframe extracts, flat files) and transform it for modern cloud consumption. • AI/DS Fluency: A strong understanding of the specific data needs for Machine Learning (feature engineering) and Generative AI (vectorization and embedding pipelines). • Execution Mindset: A "get-it-done" attitude, capable of navigating enterprise bureaucracy and technical debt to ship code at the speed required by a Transformation Office. #LI-TS1 #remote Sedgwick is an Equal Opportunity Employer and a Drug-Free Workplace. If 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. You may be just the right candidate for this or other roles.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role focuses on building and improving large-scale data collection systems used to support responsible advertising and high-quality machine learning datasets. - Contribute to the development of data pipelines, monitoring systems, and data quality tools that support large-scale advertising infrastructure and analytics platforms. Key Responsibilities - Data Pipeline Development - Design and implement large-scale data processing pipelines using SQL and Python - Build systems that support data collection, transformation, and monitoring workflows - Develop scalable data architectures that support high-volume data processing - Platform Development & Monitoring - Build dashboards and visualization tools that provide insights into data quality and system performance - Implement monitoring systems and automated alerts to detect issues in data pipelines - Create end-to-end testing frameworks to ensure reliability and data integrity - Data Quality & Debugging - Debug complex data flow issues across distributed data systems - Identify root causes of data inconsistencies and implement long-term solutions - Maintain platform reliability and ensure consistent data quality - Cross-Functional Collaboration - Work closely with engineering teams, product managers, and data specialists - Coordinate with labeling teams and vendor partners involved in data collection operations - Support targeted data collection strategies and performance optimization initiatives Qualifications - Professional experience working with SQL and large production datasets - Proficiency in Python for data processing, automation, and pipeline development - Experience building data engineering pipelines including ETL workflows and data modeling - Experience creating dashboards or data visualization tools such as Tableau or similar platforms - Strong analytical and problem-solving skills Preferred Qualifications - Degree in Computer Science or a related technical field - Experience working with machine learning datasets and model training pipelines - Experience building scalable data platforms or distributed systems - Experience working on privacy-sensitive or advertising-related systems - Experience working with human labeling or data annotation workflows Why This Opportunity - Work on high-impact data systems supporting global advertising platforms - Contribute to infrastructure that improves data quality and responsible AI systems - Collaborate with engineers, data scientists, and product teams - Gain experience working on large-scale data infrastructure used in production environments Contract Details - Full-time contract role - Fully remote with U.S. location requirements - Competitive hourly compensation - Opportunity to work on high-impact infrastructure within a leading technology organization About the Platform This opportunity is available through a leading AI-driven work platform.

• Design and operate scalable batch and streaming data pipelines • Own data ingestion, transformation, and serving layers across systems • Build reliable data platforms used by analytics, product, and AI teams • Ensure data quality, freshness, and correctness at scale • Design secure access patterns for sensitive financial data • Implement encryption, access controls, and audit-ready data workflows • Partner with product, risk, compliance, and InfoSec teams • Debug data failures, delays, and inconsistencies in production • Take ownership during data incidents and reliability issues