• Build data pipelines to create data foundation for Process Mining tools to identify bottlenecks, inefficiencies, conformance deviations, and potentials for process standardization, improvement, and automation in cooperation with process owners and process experts • Understand business needs around data integration, data engineering, and data operations to build end-to-end data pipelines for analytical purposes • Manage and troubleshoot data pipelines in Azure, Databricks and Datasphere environments • Work together with Process Mining experts to support analysis of processes

Role Description As a Data Software Engineer, you will be responsible for building new data solutions for our rapidly expanding customer base and working with the top data ingestion technologies, working with a team of amazing, diverse-minded, and bright people who make an impact, generate creative & innovative ideas, and take on new perspectives. Responsibilities - Own end-to-end data flows from requirements and architecture through implementation and production operations, including Data acquisition, Data set acceptance criteria, and Data Science integration. - Design and build scalable batch and real-time data pipelines and lakehouse solutions with a focus on large-scale data processing. - Take responsibility to explore technologies to scale up the Data ecosystem to handle rapid Big Data growth. - Partner with Data Science to productionize ML/AI workloads and ensure smooth integration into products. - Collaborate with cloud, DevOps, application, and client teams to deliver robust, secure, and scalable solutions that solve meaningful business problems. - Evaluate and adopt new technologies and patterns to evolve the data ecosystem as scale and complexity grow. Qualifications - 3+ years of hands-on Data Engineering building and operating production-grade Data Systems and Pipelines (Data-Intensive, Distributed Processing, Databases). - B.Sc. / M.Sc. in Computer Science, Computer Engineering, or equivalent. - Proficiency in Python; working proficiency in Scala. - Strong expertise with at least one major cloud provider (AWS, Azure, or GCP). - Strong experience with Big Data processing (Spark, DataBricks) and event streaming (Kafka). - Experience with orchestration and platform tooling such as Airflow; ability to build maintainable DAGs and operationalize workflows. - Strong SQL skills and experience with data storage systems plus at least one of: Data Lake/Lakehouse, columnar DB, or NoSQL systems. - Hands-on experience with containers and Kubernetes (Helm is a plus) and modern CI/CD practices. - Familiarity with LLM workflow frameworks (LangChain/LangGraph). - Proven experience designing, building, and owning production-grade data pipelines (batch and/or streaming), including reliability, backfills, and SLA-driven delivery. - Ability to learn new technologies and work in a dynamic fast-paced environment. - Result-driven, pragmatic, and innovative. - Strong analytical skills, with an open and proactive mindset to investigate, learn, and propose solutions; highly self-driven and self-taught. - Strong English communication skills, both written and verbal. Requirements - Experience with Delta Lake and/or Apache Iceberg; ML lifecycle tools such as MLflow. - Experience with Pandas/Polars and building data services/APIs (e.g., FastAPI). - Experience building LLM-powered agents / chat assistants (RAG, tool/function calling, workflow or multi-agent orchestration), using modern frameworks and platforms. - Infrastructure as Code (Terraform/Pulumi/CloudFormation) and cloud security fundamentals (IAM, secrets, encryption). - Experience with observability tooling (metrics/logging/tracing) and cost/performance optimization for distributed workloads. - Experience building applications with React and Node.js. Benefits - Flexible working environment - Volunteer time off - LinkedIn Learning - Employee-Assistance-Program (EAP)

• Deploy and harden UDS Data Capability in the mission hero's environment — stand up the UDS Store (Iceberg, Rook/Ceph, pgvector, Postgres), wire up UDS Transit for air-gap data movement, configure UDS Govern policies (Pepr/Lula), and integrate UDS Connect (Strimzi/Kafka) where streaming or legacy connectors are required. • Own the integration with what they already have — connect UDS Data Capability to whatever's already running: Big Bang, legacy Oracle and SQL Server, flat-file drops, SOAP/REST endpoints, message buses, existing object storage, identity providers (Keycloak, mission-side SSO). • Build pipelines that move data through classification boundaries — ingestion, transformation, catalog registration, model/dataset packaging via Zarf, cross-domain transit, eventual consistency across DDIL conditions. • Operate what you deploy — initial day-2 ownership: capacity, performance, backup/restore (Velero), observability (Vector/Loki), incident response, upgrade paths. Hand off to the mission hero's ops team once it's stable. • Generate accreditation artifacts — STIG evidence, cATO documentation, FIPS validation notes, policy mappings. You produce the evidence the mission hero's ISSM/ISSO needs to actually run this in IL4/IL5. • Be the voice of the mission hero back to product and engineering — file the issues, write the postmortems, propose the operator improvements, push the platform team on what's actually breaking in the field. Your field experience is the highest-signal input we have. • Train and transfer — leave the mission hero's team self-sufficient: runbooks, architecture docs, working sessions, knowledge transfer. • Grow junior Data Engineer FDEs — pair on hard problems, review integration designs before they reach the customer, and help junior engineers build judgment faster than they would alone. You're not managing anyone; you're making the team better.

• Deploy and harden UDS Data Capability in the mission hero's environment. • Own the integration with what they already have. • Build pipelines that move data through classification boundaries. • Operate what you deploy — initial day-2 ownership: capacity, performance, backup/restore (Velero). • Generate accreditation artifacts. • Be the voice of the mission hero back to product and engineering. • Train and transfer — leave the mission hero's team self-sufficient. • Grow junior Data Engineer FDEs.