• Design, develop, and maintain scalable data pipelines that support enterprise reporting, analytics, and AI workloads. • Integrate data from a variety of business systems including ERP, CRM, HRIS, Deltek Vantagepoint. • Develop ingestion, transformation, and data delivery processes that support enterprise business requirements. • Build and maintain data workflows that support both batch and near real-time processing needs. • Ensure data pipelines are reliable, maintainable, and scalable as business demands evolve. • Implement data quality controls, validation processes, and monitoring solutions. • Collaborate with Data & Information Architects to implement enterprise data models and master data strategies. • Support data classification, retention, lineage, access management, and audit requirements.

Role Description This is a remote position and can be located anywhere in the US. We are seeking a highly skilled Senior Manager, Data Engineering to design, build, and optimize modern data platforms that power enterprise analytics, reporting, machine learning, and AI initiatives. - Design and maintain scalable data platforms and pipelines. - Develop and optimize cloud-based data architectures. - Automate infrastructure provisioning using Infrastructure-as-Code tools. - Collaborate with data scientists, analysts, architects, and business leaders to gather requirements. - Troubleshoot complex technical issues. - Implement data quality controls and improve platform governance and security. - Mentor engineers and conduct code reviews. - Evaluate emerging technologies such as GenAI and machine learning. - Continuously improve data platform performance, reliability, and cost efficiency across cloud environments including AWS, Snowflake, Azure, and Databricks. Qualifications - Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related technical field. - Equivalent combination of education and relevant work experience may be considered. - Master's degree in Computer Science, Information Systems, Data Science, or related field preferred. - 10+ years of progressive experience in data engineering, analytics engineering, business intelligence, or related technical roles. - Proven track record designing and delivering enterprise-scale data platforms and analytics solutions. - Demonstrated ability to lead technical initiatives and influence architectural decisions. - Strong communication skills with the ability to work effectively with both technical and non-technical stakeholders. - Experience operating in fast-paced, highly collaborative Agile environments. - Ability to independently drive projects from concept through implementation and support. - Strong problem-solving mindset with a focus on business outcomes, automation, and continuous improvement. Requirements - Data Engineering & Architecture - Advanced proficiency in SQL and Python. - Experience building and maintaining scalable ETL/ELT pipelines. - Strong expertise in PySpark and distributed data processing. - Experience designing enterprise data models and lakehouse architectures. - Deep understanding of data warehousing, data lakes, and modern data platforms. - Experience implementing data quality, monitoring, and governance frameworks. - Cloud & Platform Engineering - Hands-on experience with AWS services such as S3, IAM, Glue, and Lake Formation. - Experience working with Snowflake, Databricks, Azure, or GCP. - Experience designing secure, scalable cloud-native data architectures. - Understanding of cloud security, identity management, and access controls. - Infrastructure & DevOps - Experience with Terraform or similar Infrastructure-as-Code tools. - Strong Git-based development practices. - Experience implementing CI/CD pipelines and deployment automation. - Knowledge of platform monitoring, optimization, and operational support. - Collaboration & Delivery - Experience partnering with business stakeholders to translate requirements into technical solutions. - Ability to advise teams on best practices, architecture, and platform capabilities. - Experience working within Agile, Scrum, Kanban, or SAFe delivery frameworks. - Strong documentation, communication, and stakeholder management skills. Benefits - Medical, Dental, Vision, Health Savings Accounts / Flexible Spending Accounts. - Life and AD&D Insurance. - 401(k). - Tuition Reimbursement. - Resources that encourage a lifetime of healthier living. - Compensation ranges from $133,200.00 - $195,000.00 annually, based on skills, experience, and education.

Role Description We're looking for a Senior Backend Engineer to lead the architecture and evolution of backend services that deploy and serve machine learning models in production. You'll work closely with ML Engineers, Platform, and Product teams to build scalable, reliable systems and drive technical direction across multiple teams. - Design and drive the long-term architecture of backend services for biometrics and ML model serving. - Collaborate with core platform and backend teams on organization-wide architectural initiatives. - Partner with business and engineering teams to design and deliver cross-cutting platform capabilities. - Lead architectural reviews, mentor engineers, and promote engineering best practices. - Build and maintain backend services for deploying and serving ML models. - Monitor service reliability, performance, and scalability in production. - Deploy and operate services on AWS using ECS + Fargate, SageMaker, or EC2 + Kubernetes. - Support real-time and batch inference workflows. - Contribute to CI/CD pipelines and deployment automation. Qualifications - Strong expertise in backend development using Java and working knowledge of Python. - Experience mentoring engineers and driving architectural decisions. - Working knowledge of Python, especially for ML-related workflows. - Hands-on experience with AWS (e.g., DynamoDB, ECS, EC2, Redis, S3, SageMaker). - Familiarity with Terraform or other infrastructure-as-code tools, and experience with CI/CD and production monitoring. - Experience with observability tools (Datadog, New Relic, etc.). - Experience with containers and orchestration (Docker, ECS, etc.). - Understanding of how ML models are deployed and served in production. - Experience with Kubernetes. Requirements - Experience with MLOps or ML platform engineering. - Experience with asynchronous programming and event-driven systems. 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.

• Lead the architecture and implementation of a medallion lakehouse in Microsoft Fabric, establishing bronze, silver, and gold layers that power self-serve analytics across the organization. • Design and build Claude projects and skills that allow marketing, product, and finance stakeholders to query and interpret data independently. • Partner with organizational leaders to define, document, and maintain a canonical business lexicon and data context that inform the self-serve BI platform and ensure consistent interpretation across teams. • Create and maintain robust data ingestion pipelines to ensure reliable, timely data flow from source systems into the lakehouse. • Build data models and BI dashboards as needed to support marketing analytics, product analytics, and financial analytics for a subscription DTC business. • Establish and enforce data governance protocols, including lineage, documentation, quality checks, and access controls. • Mentor and develop a cross-functional team of data engineers, analytics engineers, and BI analysts, setting technical standards and guiding best practices. • Collaborate with stakeholders across growth, product, finance, and clinical teams to translate business questions into scalable data solutions.