Partner with business and technology leaders to translate requirements into secure, scalable cloud architectures
Define target-state designs for safe and governed data movement from on-prem OT networks into Databricks
Evaluate and select approaches for ingesting/virtualizing historian data (especially OSI PI and AVEVA Connect)
Architect streaming, micro-batch, and batch data pipelines from edge to lakehouse
Design data layers (landing, curated, serving) aligned with Databricks lakehouse and Unity Catalog governance
Define AWS network and cloud security controls—VPC patterns, subnet designs, routing, encryption, private endpoints
Ensure Databricks E2 control plane and data plane security standards are followed, with compensating controls documented
Develop canonical time-series and asset-centric data models to support analytics and AI
Establish data quality SLAs, lineage standards, and AI data readiness frameworks
Produce ADRs, architecture blueprints, and engineering playbooks
Coach engineering teams and participate in architecture reviews
Collaborate with Security, Networking, and Compliance to validate controls and guide remediation
Measure and optimize cost, performance, and reliability across data pipelines and platforms

Extensive background architecting data solutions within operational technology (OT) environments
Expertise designing solutions for industrial/asset-centric data domains
Deep experience with OSI PI, AVEVA, or similar historian platforms
Strong hands-on knowledge of Databricks, Spark, Delta Lake, and Unity Catalog
Proven mastery of data pipeline architecture—batch, micro-batch, streaming, CDC, and edge-to-cloud patterns
Advanced data modeling experience with time-series data and asset hierarchies
Strong AWS networking and security knowledge: VPCs, subnets, routing, IAM, KMS, private connectivity
Ability to interpret and implement enterprise Databricks security guidance (E2 architecture)
Excellent communication and negotiation skills with senior business and technical leaders
Familiarity with ML/AI platform requirements such as feature stores, lineage, and observability

Data Architect – Operational Technology, Cloud

Key skills