Artificial Intelligence Data Engineer

About the Role

We're looking for a Data Platform Owner who combines strategic product ownership with deep technical architecture skills to lead our enterprise data platform's evolution toward AI-readiness. You'll own the platform roadmap, governance model, and cross-domain alignment, while personally architecting scalable data solutions — including LLM-powered products — that bridge technical depth with business value.

Key Responsibilities

Product Leadership & Platform Ownership

• Act as Product Owner for the enterprise Data Platform, owning roadmap, prioritization, governance model, and cross-domain alignment
• Lead and manage a data engineering team, coordinating backlog management, sprint execution, and release planning using Agile/SAFe methodologies
• Define and coordinate business-aligned workflows across domains, supporting a Data Mesh operating model
• Deliver integrated reporting and self-service analytics capabilities, enabling business teams to access governed data products independently

Cloud & AI Architecture

• Architect the analytics platform for AI-readiness based on business objectives, domain needs, and scalability requirements
• Design and implement Medallion Architecture (Raw/Bronze → Silver → Gold) for ingestion, cleansing, and standardization of enterprise data
• Design and implement centralized governance, metadata management, and fine-grained access control (e.g., Unity Catalog) across the analytics ecosystem
• Enable domain teams to independently create, manage, and publish governed data products, increasing organizational autonomy

Data Engineering & Platform Enablement

• Design and implement scalable data pipelines (Databricks, PySpark) ingesting and transforming heterogeneous data sources
• Build orchestration workflows (Azure Data Factory, Databricks Workflows) supporting both operational analytics and future AI initiatives
• Collaborate with analysts and business teams to improve data models feeding BI tools, increasing data trust, accessibility, and decision-making speed
• Lead incident analysis and troubleshooting to minimize downtime and business impact

Security, Governance & Compliance

• Define and operate enterprise security profiles and IAM/RBAC models across data platform and product domains
• Implement and maintain IAM role configuration synchronized with cloud RBAC
• Integrate security controls across the SDLC (dependency/license scanning, cloud security posture management, threat detection)
• Ensure auditability, compliance, and data security best practices across platform and domains

Cost Management & Platform Operations (FinOps)

• Own domain-level cost management and showback for infrastructure and data workloads
• Manage cost allocation across compute, storage, and platform services
• Monitor and optimize pipeline and compute resource consumption to support roadmap decisions

AI & Innovation

• Design and develop LLM-powered internal tools enabling natural-language interaction with governed enterprise data and documents
• Architect API-first, modular integrations across multiple LLM providers (OpenAI, Google Gemini, Anthropic) to enable rapid experimentation while avoiding vendor lock-in
• Apply prompt engineering, context management, and security practices (API key management, rate limiting, error handling) for production-grade LLM applications

Required Qualifications

• 5+ years of experience in data engineering or data architecture, including platform/product ownership responsibilities
• Proven experience designing Lakehouse/Medallion architectures on Databricks or equivalent platforms
• Strong experience with Azure data services (Azure Data Factory, Synapse, Databricks)
• Experience defining and implementing data governance frameworks and IAM/RBAC models
• Proficiency in PySpark and Python for data pipeline development
• Experience leading or managing a data engineering team
• Familiarity with Agile/SAFe methodologies and product ownership practices

Preferred Qualifications

• Hands-on experience integrating LLMs (OpenAI, Gemini, Anthropic) into enterprise data products
• Experience with Unity Catalog or equivalent data cataloging/governance tools
• Product Owner / Agile certification (SAFe 6, CSPO, or equivalent)
• Experience with FinOps practices for cloud data platforms
• Background in security tooling (dependency scanning, threat detection, posture management)
• Familiarity with Power BI or equivalent BI/reporting platforms

Nice to Have

• Experience building full-stack internal tools (React, FastAPI) for data democratization
• Master's degree in a quantitative, business, or computer science field
• Experience operating in a Data Mesh organizational model
• Background in automotive, manufacturing, or other large enterprise domains