Data Engineer

• Design and implement robust ETL/ELT pipelines
• Build and maintain Feature Stores and specialized datasets
• Develop the data pipelines required for Generative AI
• Act as the technical lead for our Snowflake data warehouse
• Manage complex, cross-platform data workflows using Airflow, Step Functions, or Azure Data Factory
• Partner directly with central IT, Database Administrators, and Security teams
• Implement automated validation and observability layers
• Drive the efficiency of our data stack by optimizing storage
• Work as a dedicated engineering partner to MLOps and Data Science teams

Data Engineer, AI

• Lead Storage and Data Migrations to AWS, Azure and Rackspace.
• Lead planning and execution of enterprise scale storage migrations with minimal downtime.
• Use tools including AWS DataSync, NetApp SnapMirror, Azure Data Box, Dell EMC XCP and vendor native replication technologies.
• Execute cutover planning, validation, and rollback procedures for clinical and enterprise workloads.
• Ensure migration activities align to resiliency, compliance and audit requirements.
• Partner with Cloud Architecture and Application teams to coordinate workload transitions and data validation.
• Own capacity, performance, lifecycle and daily operations of all storage hosted in Rackspace private cloud.
• Manage backup strategy, scheduling, and restore testing across on-premises, Rackspace, AWS and Azure workloads.
• Validate backup integrity, perform periodic restores and maintain compliance with retention policies.
• Engineer cloud native storage solutions including AWS S3, Azure Blob, Azure Data Lake.

Senior Storage and Data Engineer

• Serves as a lead in the analysis of business requirements associated with the design and implementation of highly complex, highly scalable enterprise data processes
• Designs and develops robust and efficient processes for extracting, loading and transforming data to meet the needs of enterprise scale projects
• Provides guidance and participates in activities involved in the analysis, design, development, troubleshooting, debugging and maintenance of an enterprise data management and data ecosystem
• Identifies, investigates, tracks, and resolves the most complex technical errors ensuring the solution is timely and of acceptable quality
• Establishes and enforces database standards, guidelines, procedures and quality assurance for database deliverables such as conceptual design, logical data model capacity planning, external data interface specs, data loading, data maintenance plan and security
• Reviews and analyzes data objects and models to improve data management and data access
• Designs, constructs, tests and implements extraction and transformation programs, processes and cycles for historical and current source data
• Creates technical specification for supporting enterprise data transformation processes and supplementing system documentation for ETL processes
• Performs data profiling, source-target mappings, data transformation development, optimization, testing and implementation
• This position participates and serves as a lead in maintaining the ETL environment for the company’s system-wide enterprise data warehouse.

Supply Chain Data Engineer

• Develop, design, and implement data warehousing solutions.
• Collaborate with stakeholders to gather requirements
• Perform data analysis and reporting to support decision-making.
• Ensure data integrity and quality in data solutions.

Enterprise Data Warehouse Developer – Microsoft Fabric

• Design, develop, and maintain robust data processes and solutions to ensure the efficient movement and transformation of data across multiple systems
• Develop and maintain data models, databases, and data warehouses to support business intelligence and analytics needs
• Collaborate with stakeholders across IT, product, analytics, and business teams to gather requirements and provide data solutions that meet organizational needs
• Monitor work against production schedule, provide progress updates, and report any issues or technical difficulties to lead developers regularly
• Implement and manage data governance practices, ensuring data quality, integrity, and compliance with relevant regulations.
• Collaborate on the design and implementation of data security measures, including access controls, encryption, and data masking
• Mentor other associate and intermediate data engineers as needed
• Perform data analysis and provide insights to support decision-making across various departments
• Stay current with industry trends and emerging technologies in data engineering, recommending new tools and best practices as needed
• Other duties as assigned or requested.

Senior Data Engineer

• Design the vision and blueprint of the organization's data framework to support public health informatics objectives.
• Design and manage data systems, establishing policies for how data is stored, accessed, and governed.
• Coordinate various data sources within the organization to ensure consistency and integrity across the data environment.
• Integrate new data technologies into existing IT infrastructures.
• Perform data modeling and design activities to support reporting, analytics, and operational needs.
• Develop and maintain ETL pipelines to support data movement and transformation.
• Write and optimize SQL queries in support of data operations and system requirements.
• Administer database environments in coordination with application and infrastructure teams.
• Submit weekly status reports detailing completed and ongoing project activities, including risks as applicable.
• Maintain accurate records of work performed and hours expended in accordance with OTS Task Order requirements.

Data Architect

• Design and develop data serving and semantic layers to support business needs
• Develop data models for use by analysts and applications using modern dimensional modeling practices
• Build and maintain robust, scalable data pipelines using ETL, ELT processes
• Create efficient and performant data models to support business requirements
• Implement and automate data quality checks and monitoring to ensure data accuracy
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis
• Collaborate with data analysts, data scientists, and business stakeholders to understand their data needs

• Design, develop, and maintain robust data processes and solutions to ensure the efficient movement and transformation of data across multiple systems
• Develop and maintain data models, databases, and data warehouses to support business intelligence and analytics needs
• Collaborate with stakeholders across IT, product, analytics, and business teams to gather requirements and provide data solutions that meet organizational needs
• Monitor work against production schedule, provide progress updates, and report any issues or technical difficulties to lead developers regularly
• Implement and manage data governance practices, ensuring data quality, integrity, and compliance with relevant regulations.
• Collaborate on the design and implementation of data security measures, including access controls, encryption, and data masking
• Mentor other associate and intermediate data engineers as needed
• Perform data analysis and provide insights to support decision-making across various departments
• Stay current with industry trends and emerging technologies in data engineering, recommending new tools and best practices as needed

Senior Data Engineer – Cloud Data Products, Analytic Enablement

• Lead and grow a team of data quality engineers and test data engineers
• Design and build reusable data quality frameworks integrated into Databricks pipelines
• Develop standardized validation libraries (schema checks, freshness, volume, anomaly detection)
• Define and enforce data quality standards and “Definition of Done” across pipelines
• Build scalable solutions for synthetic data generation and masking
• Develop replay frameworks for validating pipeline changes against production scenarios
• Create automated regression testing harnesses for batch and streaming pipelines
• Establish monitoring, alerting, and SLA/SLO frameworks for data pipelines
• Build tools to measure and track data quality, pipeline health, and reliability metrics
• Enable proactive detection of data issues in production environments
• Partner with Data Engineering, Product, and Analytics teams to embed quality practices
• Drive adoption of frameworks and standards across global engineering pods
• Define and execute a long-term roadmap for enterprise data quality
• Prioritize initiatives across multiple stakeholders and global teams
• Eliminate manual validation processes through automation and engineering best practices

Manager, Test Data Engineering Team

• Design, build, and maintain scalable Snowflake-based data warehouse architectures, ensuring performance, reliability, and extensibility for future growth.
• Own data architecture and technical design decisions, including data models, integration patterns, and optimization strategies.
• Transform raw source data from multiple internal and external systems into analytics-ready data models using industry best practices.
• Lead and contribute to warehouse development using dimensional modeling techniques (e.g., star schemas, slowly changing dimensions).
• Develop and manage data pipelines using Snowflake features such as Snowpipe, tasks, stored procedures, and performance optimization techniques.
• Manage and monitor data integrations with internal systems via APIs, application calls, or custom code to ensure data accuracy and reliability.
• Apply auditing, change tracking, error handling, and monitoring across large datasets to maintain high data quality standards.
• Identify and resolve data quality issues and implement controls to distinguish valid vs. invalid data used in decision-making.
• Leverage Python and application knowledge to interact with and extract data from other systems.
• Provide technical leadership through coaching and mentoring junior and mid-level data engineers, including code reviews, design feedback, and best-practice guidance.
• Contribute to and help define data engineering standards, best practices, and documentation across the team.
• Collaborate across teams to support day-to-day operational data needs and continuously improve data pipelines and processes.

• CSAA Insurance Group is seeking a Principal Architect to serve as our Enterprise Data Architect
• Supporting the design and delivery of secure, scalable data and AI platforms across the enterprise
• Facilitating designs and building solutions across the enterprise
• Solutions are built in a highly collaborative environment, partnering with engineering, product, and program leaders
• Leads and provides direction to Application Integration, Data, and Platform architecture vision for the enterprise
• Integrates a broad range of ideas regarding technical and solution architecture
• Creates, guides, and partners with other architects in creating and maintaining roadmaps that connect business strategies to capabilities
• Enables AI/ML use cases by supporting data pipelines, feature engineering, and model integration
• Balances speed and control to support business innovation

Enterprise Data Architect

• Own the enterprise data standards
• Establish and chair data design reviews
• Define reference architectures and canonical models
• Author data integration patterns and quality standards
• Set ingestion/promotion standards for analytics platform
• Encode data policy requirements into architecture artifacts
• Maintain architecture and environment documentation

Senior Manager, Data Architecture

• Design the vision and blueprint of an organization’s data framework.
• Design and manage data systems, set policies for how data is stored and accessed, coordinate various data sources within an organization, and integrate new data technologies into existing IT infrastructures.
• Data modeling and design, including software development and database administration.

Data Architect – Data Ecosystems

• Support the Louisiana State Office of Technology in designing and managing a comprehensive data framework.
• Establish the vision and blueprint for the organization's data ecosystem.
• Lead efforts in data modeling and design, software development support, and database administration.
• Write and maintain complex SQL queries to support reporting, analysis, and data operations.
• Collaborate with stakeholders to identify data requirements and translate them into scalable architectural solutions.

• Joining each and every one of your colleagues in creating an inclusive workspace;
• Owning, maintaining, and supporting the reliability, architecture, and evolution of our production machine learning and delivery platform;
• Designing, operating, and improving large scale Databricks/Spark pipelines and workflows
• Partner with our data scientists to productionize new data and ML-powered products;
• Leading improvements on the software integration layers between data pipelines and product workflows;
• Growing the team via mentorship and leadership, reviewing technical documents, improving code quality, and improving team performance and processes;
• Acting as a senior technical owner in debugging complex production issues and improving system observability;
• Collaborating with cross-functional teams to support their data infrastructure needs.

Senior Software Engineer, Data Platform

• Lead, manage, and develop a team of data engineers, fostering a culture of technical excellence, independence, collaboration, and accountability.
• Create and execute a Data Engineering roadmap that helps ReUp to achieve its goals.
• Partner with technology, analytics, finance, B2B, and B2C teams to define data requirements, and design and develop solutions that drive value for ReUp’s clients and learners.
• Oversee the design, development, testing, implementation, maintenance, and continuous improvement of data pipelines and warehouses that ingest, transform, store, and provide access to student and institutional data to power business intelligence products, ML models, cloud-based web applications, and marketing and coaching services.
• Champion automation, observability, and performance to ensure ReUp’s pipeline efficiently and reliably delivers quality data to end users.
• Proactively identify, triage, and resolve data quality and availability issues through root-cause analysis, remediation, monitoring, and cross-functional collaboration.
• Define and monitor SLAs for data quality, workflow status and runtime, incident response and resolution time, and team velocity.
• Define and implement data engineering standards and best practices.
• Support data security, compliance, and governance initiatives.

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