Data & Analytics Engineer building scalable data platforms for Ledgebrook, an insurance company. Designing pipelines, governance, and cloud data systems while partnering with technical and insurance teams.
About the role
- Staff Data Engineer designing AI-ready data platforms for Robots and Pencils, an applied AI engineering firm. Leading pipelines, governance, modernization, and production AI/ML data systems.
Responsibilities
- Define data architecture and platform strategy across pipelines, warehouses, and data lakes
- Build and optimize scalable batch and real-time data pipelines
- Define and enforce data governance, quality standards, and compliance frameworks
- Build monitoring, logging, and alerting for data pipelines and services
- Contribute to CI/CD workflows for data deployment and automation
- Drive data platform modernization for performance, cost, and scalability
- Use Claude, Cursor, and other modern AI assistants to ship higher-quality work
- Design and implement data contracts and event flows with backend, platform, and engineering teams
- Lead data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training/inference data flows
- Integrate data services with APIs, middleware, and third-party systems
- Partner with leadership on data strategy
- Collaborate with engineering, analytics, AI, and product teams
- Advocate for data quality, governance, and platform best practices
- Establish data engineering standards across the team
- Mentor junior and mid-level engineers
- Make high-stakes architectural decisions with clear ownership and consideration of long-term tradeoffs
Requirements
- 7+ years of professional data engineering experience, including leading complex data platform initiatives
- Strong system architecture background and expertise in distributed data systems
- Expert proficiency in Python, Scala, and SQL
- Deep expertise with cloud-native data platforms and enterprise data warehousing
- Strong expertise in data pipeline orchestration and processing
- Strong experience with streaming platforms and real-time data processing, such as Kafka, Kinesis, or Pub/Sub
- Strong data modeling expertise and experience with data transformation
- Strong experience with data quality, governance, and compliance frameworks
- Strong experience with container orchestration and CI/CD for data systems
- Strong experience building data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training/inference data flows
- Demonstrated leadership and technical mentoring experience
- Strong stakeholder communication skills and ability to translate technical depth across audiences
- Demonstrable day-to-day usage and expert knowledge of AI-forward coding tools such as Claude and Cursor
- Excellent problem-solving skills and sound judgment in ambiguous technical and business challenges
- Experience with data mesh or data fabric concepts, lakehouse architectures, or governance framework implementation is a plus
- Experience handling and modeling healthcare-industry data is a plus
- AWS certifications, such as Certified Data Engineer – Associate, are strongly preferred
- Employment may be conditional upon successful completion of a background check in accordance with local legislation
Benefits
- Potential employment offer conditional upon successful completion of a background check in accordance with local legislation
- Equal employment opportunity commitment
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