Engineer II, Data Engineer

Job Summary Role Overview Why This Role Exists

• Owns a data domain end-to-end, with clear accountability for the pipelines, models and datasets digital workers build and operate in it, and sound independent judgement on design choices.
• Makes digital workers more effective in that domain by improving the frameworks, tools and evaluations they use, so manual intervention falls measurably over time.
• Delivers quality-assured data to AI systems through data contracts, governed context and quality frameworks that hold up to audit.

Key Responsibilities Agent-Operated Data Engineering

• Design pipelines for complex use cases and direct digital workers to build and operate them, with clear, defensible architectural trade-offs.

Own the quality of agent-generated work in your domain

• Review, test and approve it within the remit EAIs operating model sets, and escalate what falls outside it.

Improve how digital workers operate in your domain

• By refining the frameworks, tools and evaluations they use, iterating on reliability signals so manual intervention falls measurably over time.

Context & Semantics

• Own the context for your domain in the governed context layer: definitions, contracts, lineage and governance documentation that humans and digital workers rely on.
• Contribute reusable components to the context layer, such as metadata enrichment, classification or lineage capture, that other domains can adopt.

Data Quality & Governance

• Build an end-to-end data quality framework for your domain, with lineage tracking and governance documentation that passes audit.
• Apply privacy and consent controls in AI data pipelines and manage PII appropriately across the data lifecycle.

AI Data Infrastructure & Lifecycle

• Design and build feature stores, vector databases or retrieval infrastructure for production AI systems, with data contracts that give each AI consumer consistent, quality-assured inputs.
• Manage training datasets, evaluation sets and data versioning for AI projects in production, and contribute to evaluation dataset design with a clear view of how data decisions affect model quality.

Platform & Infrastructure

• Build cloud data infrastructure independently using infrastructure as code and contribute reusable components to the teams data platform.
• Design observability into pipelines from the start and resolve infrastructure issues with minimal disruption to consumers. Own incident response for your domain, with clear post-incident analysis and follow-through.

Collaboration & Standards

• Raise team technical quality through code review and documentation, with specific improvements to shared practices.
• Work closely with forward deployed engineers (FDEs) and AI engineering teams, translating ambiguous requirements into well-scoped designs and delivering data assets on time and to agreed quality standards.
• Contribute to design reviews with clear trade-off reasoning and communicate technical choices clearly to engineering and non-technical stakeholders.

Required Experience

• 10+ years of experience building and operating production data pipelines and data models.
• Deep SQL and Python, with strong hands-on expertise in modern warehouses or lakehouses and in orchestration and transformation frameworks, such as Databricks, BigQuery, Airflow, dbt or Spark.
• Experience designing and building feature stores, vector databases or retrieval infrastructure for production AI systems, including data contracts for at least one AI consumer.
• Experience managing training datasets, evaluation sets and data versioning for at least one AI project in production.
• A track record of building data quality frameworks and lineage for a domain, and of applying privacy, consent and PII controls in AI data pipelines.
• Experience building cloud data infrastructure independently with infrastructure as code, with observability designed in from the start.
• Experience designing and operating at least one AI agent or LLM-powered tool inside a data workflow, and fluent, daily use of AI coding assistants and agents with sound judgement on what to accept, change or reject.

Preferred Experience

• Led the design and delivery of a component of a larger data platform.
• Experience with agentic systems in production: multi-step tool use, evaluations, guardrails and observability applied to operational data problems.
• Experience with quality frameworks and with catalogue, metadata or lineage tooling.
• Experience in regulated or high-stakes data environments, such as life sciences, finance or healthcare.

Education

• Bachelors degree in Computer Science, Data Engineering or a related field, or equivalent practical experience.

Skills

• Agentic AI
• Computer Programming
• Data Analysis
• Databricks Platform
• Data Engineering
• Data Governance
• Data Ingestion
• Data Integration
• ETL Development
• Performance Tuning
• Python (Programming Language)
• SQL Query Development
• Structured Query Language (SQL)

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