Job description

Position Summary RaceTrac is seeking a Lead Data Architect to drive enterprise data modernization, Databricks adoption, AI enablement, and real-time data platform transformation. This is a hands-on leadership role responsible for defining architecture strategy, guiding engineering teams, and delivering scalable data solutions that support analytics, AI/ML, and business growth. The ideal candidate will have deep expertise in Databricks, Azure/AWS, data architecture, MDM, streaming technologies, and AI engineering, with a track record of leading large-scale modernization initiatives. Key Responsibilities Lead migration of legacy SQL/Data Warehouse platforms to Databricks Lakehouse architecture. Design scalable cloud-native data platforms supporting analytics, reporting, AI/ML, and GenAI use cases. Define architecture patterns for batch, streaming, API, and event-driven data processing. Establish data engineering standards, governance, observability, security, and FinOps practices. Design trusted data foundations, including MDM, data quality, metadata, and data governance capabilities. Partner with engineering, analytics, and business teams to solve complex technical challenges and drive implementation. Architect AI-ready platforms supporting feature stores, RAG, vector databases, AI agents, and MLOps. Lead architecture reviews, technical workshops, and technology evaluations. Required Qualifications 12+ years of experience in Data & Analytics. 5+ years leading enterprise data architecture and modernization initiatives. Strong hands-on experience with Databricks, Spark, Delta Lake, Azure, and AWS. Proven experience migrating traditional SQL/Data Warehouse environments to modern cloud data platforms. Experience designing MDM solutions, including mastering and golden-record frameworks, with or without commercial MDM tools. Strong knowledge of real-time and streaming architectures (Kafka, CDC, event-driven patterns). Experience supporting AI/ML and GenAI initiatives. Retail, Convenience Retail, Consumer Goods, or related industry experience preferred. Key Areas of Evaluation Candidates should demonstrate practical experience in: Human-in-the-Loop / Agent-in-the-Loop AI Engineering Migration from SQL/Data Warehouse platforms to Databricks Implementing MDM capabilities within a Data Warehouse/Lakehouse environment without dedicated MDM products Modern streaming architectures and the evolution from legacy batch-based approaches to real-time/event-driven platforms This role is best suited for a hands-on architect who can design, challenge, and implement modern data and AI platforms while partnering closely with engineering teams to drive execution.

Requirements

Must-haves

The ideal candidate will have deep expertise in Databricks, Azure/AWS, data architecture, MDM, streaming technologies, and AI engineering, with a track record of leading large-scale modernization initiatives.
Lead migration of legacy SQL/Data Warehouse platforms to Databricks Lakehouse architecture.
Design scalable cloud-native data platforms supporting analytics, reporting, AI/ML, and GenAI use cases.
Define architecture patterns for batch, streaming, API, and event-driven data processing.
Establish data engineering standards, governance, observability, security, and FinOps practices.
Design trusted data foundations, including MDM, data quality, metadata, and data governance capabilities.
Partner with engineering, analytics, and business teams to solve complex technical challenges and drive implementation.
Architect AI-ready platforms supporting feature stores, RAG, vector databases, AI agents, and MLOps.
Lead architecture reviews, technical workshops, and technology evaluations.
12+ years of experience in Data & Analytics.
5+ years leading enterprise data architecture and modernization initiatives.
Strong hands-on experience with Databricks, Spark, Delta Lake, Azure, and AWS.
Proven experience migrating traditional SQL/Data Warehouse environments to modern cloud data platforms.
Experience designing MDM solutions, including mastering and golden-record frameworks, with or without commercial MDM tools.
Strong knowledge of real-time and streaming architectures (Kafka, CDC, event-driven patterns).
Experience supporting AI/ML and GenAI initiatives.
Key Areas of Evaluation
Candidates should demonstrate practical experience in:
Human-in-the-Loop / Agent-in-the-Loop AI Engineering
Migration from SQL/Data Warehouse platforms to Databricks
Implementing MDM capabilities within a Data Warehouse/Lakehouse environment without dedicated MDM products
Modern streaming architectures and the evolution from legacy batch-based approaches to real-time/event-driven platforms
This role is best suited for a hands-on architect who can design, challenge, and implement modern data and AI platforms while partnering closely with engineering teams to drive execution.

Nice-to-haves

Retail, Convenience Retail, Consumer Goods, or related industry experience preferred.

Application prep

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Keywords to mirror

aimlazureawssqlapi

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