• 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 Data Architect serves as an experienced individual contributor within a team, with the expectation that you will further develop your leadership, guidance, and mentoring skills. • With minimal oversight from leadership, you will be responsible for supporting the goal of meeting scope, schedule, and delivery requirements. • A Senior Data Architect impacts the long-term goals of the program, while contributing to the development of the program's data architecture strategy. • You may serve as the discipline's primary lead when working with stakeholders and utilize strong influential skills to drive improvements in data architecture processes and practices. • Lead complex legacy data migrations end-to-end, including schema mapping, data profiling, phased migration sequencing, parallel-run validation, and cutover with automated rollback procedures. • Design and maintain cloud-native data pipelines using event-driven infrastructure (SNS/SQS, Lambda/Step Functions) to support BI and analytics workflows. • Architect data analytics repositories that are production-ready, monitored, and fully documented so agency staff and external tools can operate without contractor dependency. • Manage digital artifact and content management lifecycles including upload, storage, review, and archiving across AWS and Azure/M365 environments. • Drive data governance across programs, including lineage documentation, dictionary standardization, and glossary development. • Align data architecture and standardize access patterns to make data available to BI tools, apps, and web platforms. • Translate business and agency requirements into functional BI portals and dashboards. • Present architectural decisions clearly to mixed audiences, from engineers to product owners to federal agency stakeholders. • Mentor junior analysts and engineers and participate in technical interviews and candidate evaluations. • May lead small, less critical, or temporary structures and projects.

• Data Engineer III serves as an emerging individual contributor within a team, expanding your leadership, guidance and mentoring skills. • With the support and guidance of leadership, you will be responsible for supporting the goal of meeting scope, schedule and delivery requirements. • You will interact with stakeholders and utilize influential skills to drive improvements in data engineering processes and practices. • Builds data infrastructure, pipelines, systems, and tools with a little feedback and guidance from their team. • Intermediate understanding of data management and transformation of raw data into interpretable information. • Ability to create accurate and accessible datasets for Data Analysts and Scientists to interpret and use. • Own maintenance of data architectures and systems. • Practices technical and communications skills to improve context knowledge and mentor junior data engineers. • Presents on and writes about their work to both internal and external parties. • Participates in planning sessions, building an understanding of data architectures and infrastructure. • Supports recruiting efforts by evaluating homework assignments and potentially assisting with interviews.

Role Description At Jumio, we’re building trusted identity solutions powered by machine learning and biometrics. As a Data Engineer II, you will take ownership of designing, building, and scaling data pipelines that power our ML systems. You’ll work closely with ML engineers, platform teams, and product stakeholders to enable robust dataset creation, benchmarking, and production-grade data workflows on AWS. - Design, build, and maintain scalable and reliable data pipelines for dataset creation, transformation, and benchmarking. - Own and optimize Airflow pipelines on AWS for data processing, orchestration, and evaluation workflows. - Write efficient, production-grade SQL and Python code for large-scale data processing and analysis. - Partner closely with ML engineers to enable model training, evaluation, and benchmarking pipelines. - Improve pipeline performance, reliability, and observability, ensuring high data quality in production. - Build and maintain systems to support model performance tracking and data drift monitoring. - Troubleshoot and resolve data issues across pipelines, ensuring minimal impact on ML workflows. - Contribute to data architecture decisions and best practices across the platform. - Collaborate cross-functionally with ML, platform, and data teams to support scalable ML infrastructure. Qualifications - 3–5 years of experience in Data Engineering, Data Platforms, or related roles. - Strong proficiency in Python and SQL with experience in production systems. - Hands-on experience with AWS services (S3, EC2, SageMaker or similar). - Solid experience building and managing Airflow (or similar orchestration tools). - Strong understanding of data engineering fundamentals (ETL/ELT, data modeling, pipeline design). - Experience working with large-scale datasets and distributed data systems. - Experience supporting ML workflows, datasets, or evaluation pipelines. - Strong problem-solving skills and ability to work independently in a fast-paced environment. Requirements - Experience with ML infrastructure, MLOps, or model evaluation workflows (Nice to Have). - Exposure to biometric systems or computer vision datasets (Nice to Have). - Familiarity with data quality frameworks, monitoring, and observability tools (Nice to Have). - Experience working in SaaS or high-scale production environments (Nice to Have). Benefits - IDEAL: Integrity, Diversity, Empowerment, Accountability, Leading Innovation. Company Description Jumio is a B2B technology company dedicated to eradicating online identity fraud, money laundering, and other financial crimes to help make the internet safer. We leverage AI, biometrics, machine learning, liveness detection, and automation to create solutions that are trusted by leading brands worldwide and respected by industry thought leaders. Jumio is the leading provider of online identity verification, eKYC, and AML solutions. With a global footprint, we’re expanding the team to meet strong client demand across a range of industries including Financial Services, Travel, Sharing Economy, Fintech, Gaming, and others.