About the role
- As a Senior Data Engineer, you will manage the “discovery-to-scale” pipeline, identifying recurring patterns in the field and handing off validated solutions to Platform Engineers for enterprise generalization
- In this forward-deployed role, you will bridge the gap between building technical products and solving real-world business problems through rapid solution delivery and deep domain immersion
- Your primary objective is to design and build data integration, storage systems, and infrastructure that enable analytics and AI capabilities
- This role requires high-level autonomy, where you will define your own direction within strategic goals, influence multiple teams, and navigate high complexity and ambiguity to deliver tangible business results- Full-Stack Solution Delivery: Capability to rapidly prototype and build complete, end-to-end data applications across diverse technology stacks, prioritizing solution speed and business value while effectively managing technical debt
- Experience & Education: Bachelor’s degree in Computer Science or Engineering with 8–12 years of relevant experience leading and delivering complex data engineering projects with multi-team or business impact
- Advanced Data Modeling: Strong skills in conceptual, logical, and physical data modeling, with the ability to balance data normalization, query performance, and safely manage complex schema migrations in production
- Cloud-Native Data Ecosystems: Deep expertise in deploying and managing data infrastructure on cloud platforms (AWS, Azure, or GCP). Proficient in Infrastructure as Code (Terraform/CloudFormation) and establishing end-to-end CI/CD pipelines for data solutions
- System Reliability & Resilience (SRE): Direct experience defining and monitoring service level objectives (SLOs/SLIs). A track record of designing highly resilient data systems that ensure graceful degradation and driving post-incident improvements
- Enterprise Data Integration & Architecture: Proven expertise in designing, building, and maintaining robust, scalable data pipelines (ETL/ELT). Must demonstrate the ability to navigate complex enterprise data landscapes, effectively handle undocumented schemas, and implement resilient integration solutions for high-volume data
- Architectural Leadership: Ability to make sound architectural trade-offs, design scalable system components, and produce high-quality, well-tested code that adheres to enterprise standards
- AI/MLOps Readiness: Mastery of incorporating AI tools to optimize data engineering workflows and the ability to design data validation and evaluation frameworks that support the deployment and governance of AI/ML models
- Systems Thinker: You map complex system interactions across technical and business domains, anticipating cascading effects and understanding how technology changes impact operations
- Curious Explorer: You seek out ambiguity rather than avoiding it, driving team curiosity through challenging questions and creating an environment that encourages experimentation
- Precise Communicator: You translate seamlessly between technical and business language, ensuring requirements are clear enough to enable AI generation and facilitate productive discussions with stakeholders
- Outcome Owner: You drive an accountability culture focused on business impact rather than just deliverables, owning relationships and making trade-offs between custom solutions and generalizable work
- Polymath Oriented: You bridge gaps between engineering, design, business, and science, rapidly immersing yourself in new domains to speak the language of the business
- Problem Discoverer: You embed with users to discover latent needs, turning ambiguity into clear problem statements rather than waiting for defined tasks