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.

Role Description We are looking for a strategic and hands-on Data Domain Engineering Director to lead domain-based data product teams within Global Digital Engineering. In this role, you will be responsible for the technical delivery, operational reliability, and continuous evolution of high-value data products built on a unified data platform. You will work as a peer and strategic partner to business data domain leads, translating business priorities into scalable, reliable, and reusable data products. - Own the end-to-end lifecycle of domain data products, including design, development, operationalization, evolution, and retirement. - Lead technical delivery across data ingestion, pipelines, transformations, APIs, and data engineering processes within the domain. - Build data products using the unified data platform and ensure consistency and quality through a standardized data product journey. - Define and enforce domain-level data product standards in line with enterprise governance, interoperability, and quality requirements. - Ensure discoverability, usability, documentation, metadata quality, and adoption of domain data products. - Take accountability for operational reliability and stability, including service level objectives, monitoring, and continuous improvement. - Partner closely with business domain owners as a technical co-leader, ensuring equal standing and direct collaboration on priorities and outcomes. - Coordinate with other data domain engineering directors, the data platform leadership, and data foundation teams to enable consistent and seamless delivery. - Communicate technical decisions, risks, dependencies, and progress clearly to senior stakeholders and management. - Establish and continuously improve release, operations, quality, and engineering processes. - Develop and lead high-performing data engineering and data product teams through hiring, coaching, staffing, and capability development. - Manage domain-level technical debt and architectural evolution, balancing delivery speed with long-term maintainability and scalability. Qualifications - Strong expertise in data product architecture and domain-oriented data product design. - Proven experience building and operating scalable data products on modern cloud-based data platforms. - Deep understanding of data engineering practices, including data ingestion, pipelines, transformations, and API-based data access. - Ability to ensure operational stability, reliability, and quality of data products through service level objectives, monitoring, and observability. - Strong understanding of data product management, including product value, consumers, lifecycle, adoption, and service levels. - Ability to translate business priorities into domain data products aligned with measurable business outcomes. - Experience working in federated data operating models and domain-based data ownership structures. - Ability to drive interoperability, standardization, and reuse of data products across domains. - Experience managing cross-domain dependencies and prioritization in a Data Mesh environment. - Proven leadership experience managing data engineering and data product teams. - Experience staffing, developing, and coaching teams with the right mix of engineering, product, and domain capabilities. - Ability to operate as a technical co-leader and strategic partner to business domain owners. - Strong stakeholder management skills and the ability to align business priorities with technical delivery. - Experience driving cross-domain collaboration in federated data organizations. - Ability to build a strong data product mindset and ownership culture focused on accountability, quality, and continuous improvement. Additional Information - Please be informed that our remote working possibility is only available within Hungary due to European taxation regulation.