Data Engineer III

Job not on LinkedIn

🕒 May 24

🏄 California – Remote

🏄 California – Remote

💵 $104k - $153k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

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Dyson

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1991

🔧 Hardware

🛒 Retail

Hardware • Manufacturing • Retail

Dyson is a unique technology enterprise known for its innovation and engineering superiority. Originating from a small workshop in rural England, Dyson has grown into a global powerhouse with offices worldwide, from Auckland to Zurich, Shanghai to Chicago. The company's core revolves around engineering but extends to pioneering in diverse technology sectors, including energy storage, robotics, and machine learning. Dyson's products are renowned for quality and innovation, with a strong focus on design and development, which reflects across their vibrant global offices.

📋 Description

• Lead architecture and design of complex data pipelines on Databricks lakehouse architecture (Unity Catalog, Delta Lake, Structured Streaming) • Define technical approach for data engineering initiatives, mentor less-senior engineers, and set standards for code quality through leadership and code reviews • Design and build data foundations that enable AI/ML capabilities — feature stores, embedding pipelines, vector search indexes, and model training datasets • Align data engineering solutions with business strategy, including support for Agentic AI workloads • Own health, scalability, and modernization of data infrastructure with Databricks as the strategic platform — including workload migration, compute optimization, and Unity Catalog adoption • Optimize pipeline performance (Delta Lake table layouts, clustering, Z-ordering) and establish monitoring/alerting best practices with clear SLAs • Build data infrastructure supporting Agentic AI systems — real-time data access layers, context retrieval pipelines, and agent-accessible data services • Collaborate cross-functionally with DevOps, Platform Engineering, and MLOps roles to integrate data solutions into the broader technology environment and shared AI infrastructure – Mlflow registries, feature stores, and agent orchestration layers • Provide consultation to Senior Leadership on complex projects and drive continuous improvement initiatives • Champion data governance at all layers for data, models, and AI assets • Implement data quality strategies (master data management, validation rules, Delta Live Tables expectations) to ensure trust in enterprise data • Serve as liaison across data engineering, AI engineering, and business teams; promote data literacy and stewardship

🎯 Requirements

• Bachelor's in Computer Science, Engineering, or related field (Master's preferred) • 5+ years with Python and SQL in data engineering for big data ML/analytics workloads • 5+ years designing, building, and troubleshooting scalable ETL/ELT pipelines for business-critical production systems • 3+ years with cloud data services (AWS), container orchestration (Docker, Kubernetes), and IaC (Terraform, CloudFormation) • 3+ years architecting ML workflows and data platforms with CI/CD, automated testing, and distributed processing (Spark) • 3+ years collaborating cross-functionally with Data Science, MLOps, Platform Engineering, and DevOps teams • 3+ years implementing data quality testing and optimizing SQL/Python for cost/performance in the cloud • Understanding of the full Data Science SDLC, and experience mentoring engineers • Strongly Preferred - Databricks & AI Platform • 2+ years hands-on with Databricks (Delta Lake, Unity Catalog, Databricks SQL) • Experience with MLflow experiment tracking and model registry workflows • Experience designing pipelines that serve AI/ML inference — real-time feature engineering, embedding generation, and context retrieval for LLM-based systems • Understanding of how data engineering supports Agentic AI: agent-accessible data services, low-latency retrieval, and pipelines enabling autonomous multi-step workflows • Familiarity with Databricks Mosaic AI, Vector Search, and/or Feature Store • FinOps awareness — compute cluster optimization, cost attribution by workload • Familiarity with Salesforce/Heroku data infrastructures • Experience with data virtualization (e.g., Dremio) • Understanding of Platform Engineering concepts and internal developer platforms • Experience migrating from legacy data warehouse/lake to unified lakehouse architecture • Familiarity with Odaseva data security and management

🏖️ Benefits

• group health insurance benefits (medical, vision, dental) • FSA and HSA healthcare accounts • life and accident insurance • adoption and fertility assistance • paid parental leave of up to 6 weeks • short/long term disability • paid time off for vacation, personal needs, and sick time • up to 17 days of Choice Time Off (CTO) per calendar year • up to 11 paid holidays per calendar year • opportunity to contribute to company's 401(k) savings and investment plan or deferred compensation plan with an employer match of 100% on the first 3% of contributions