This is where you and your skills come in. We are embarking on a transformative initiative to modernize our enterprise data ecosystem. This modernization will serve as the foundation for our next-generation analytics, machine learning, and AI capabilities. 

We are seeking a Lead Systems & Data Architect with deep hands-on technical expertise and strategic vision to design and lead the next generation of our enterprise data platform. This role will own the architecture, roadmap, and execution of a modern, cloud-native data ecosystem that supports analytics, real-time processing, and advanced AI/ML and LLM workloads at scale.

This is a highly visible, high-impact role that sits at the intersection of data engineering, cloud infrastructure, analytics, and AI. You will serve as the technical authority for data architecture decisions while mentoring teams and influencing senior stakeholders across the organization.

If you thrive in complex modernization initiatives, enjoy solving hard distributed systems problems, and want to shape a platform that will power data-driven and AI-enabled products for years to come, this role is for you.

What You’ll Do

Architecture & Strategy

• Define and own a cloud-native, enterprise-scale data architecture that supports batch, streaming, analytics, and AI/ML workloads.

• Develop and execute a multi-year data platform modernization roadmap, balancing delivery speed, risk, cost efficiency, and business continuity.

• Drive architectural decisions across Snowflake, Databricks, and cloud-native services, ensuring scalability, security, and performance.

Platform & Engineering Leadership

• Partner closely with data engineering, analytics, infrastructure, security, and data science teams to design scalable ingestion, transformation, and serving layers.

• Define standards and best practices for data ingestion, transformation, integration, and data modeling across diverse structured and semi-structured sources.

• Establish robust data governance, quality, lineage, and access control frameworks aligned with enterprise and regulatory needs.

• Optimize performance and cost efficiency across Snowflake and Databricks environments, including workload isolation, query optimization, and storage strategies.

DevOps, Reliability & Security

• Lead the adoption of DevOps, CI/CD, and Infrastructure as Code practices (e.g., Terraform) for data platforms.

• Define patterns for environment management, observability, disaster recovery, and security-by-design.

• Ensure the platform meets enterprise standards for data privacy, compliance, and resilience.

AI / ML & Future Readiness

• Design the data foundation required for advanced analytics, ML, and LLM workloads, including feature stores, vector storage, and real-time data access.

• Stay ahead of emerging trends in AI, ML, and data platforms, translating innovation into practical, production-ready architectures.

• Influence the evolution toward lakehouse, data mesh, and data product–oriented architectures where appropriate.

Leadership & Influence

• Provide technical leadership and mentorship to senior data engineers and architects.

• Act as a trusted advisor to engineering leadership and business stakeholders.

• Clearly communicate complex architectural concepts to both technical and non-technical audiences.

Required Qualifications

• 10+ years of experience in data architecture, data engineering, or platform engineering roles.

• Proven success leading large-scale data platform migrations, including:

  • On-prem data warehouses (e.g., Oracle, Teradata) to Snowflake

  • Hadoop ecosystems (HDFS, Hive, Spark) to Databricks

• Deep hands-on expertise with SQL, Spark, and Python, and strong understanding of distributed data processing.

• Strong experience designing and operating data platforms on AWS, Azure, or GCP, including compute, storage, networking, and security.

• Solid background in data governance, metadata management, lineage, and cataloging (e.g., Purview, Collibra, Alation).

• Experience with real-time and streaming architectures (Kafka, Kinesis, Pub/Sub) and orchestration tools (Airflow, dbt).

• Demonstrated ability to lead cross-functional teams and complex migration programs.

• Exceptional communication skills and executive-level presence.

Nice to Have

• Snowflake, Databricks, or cloud provider certifications (AWS, Azure, GCP).

• Hands-on experience building ML and AI pipelines on Databricks or similar platforms.

• Experience with multi-petabyte-scale data platforms.

• Familiarity with data mesh, lakehouse, and domain-oriented data product concepts.

• Exposure to LLM pipelines, vector databases, or retrieval-augmented generation (RAG) architectures.