Manager, Unified Data Platform & AI Engineering

This strategic role will own the design, build, and run of AdvanSix’s digital data platform, spanning IT and OT, and the engineering lifecycle for advanced analytics and AI applications. This leader manages a 3–5 person team and coordinates embedded vendor pods to deliver a governed Unified Data Layer, robust data products, and production-grade ML services that power operations and corporate functions.

Platform & Architecture

• Own the UDL reference architecture.
• Define and operate secure OT/IT integration patterns.
• Partner with SAP teams to integrate S/4HANA and SAP DataSphere into the semantic layer strategy.

Data engineering & productization

• Lead engineering for ingestion (batch/CDC/streaming), transformation (SQL/PySpark), testing, observability, and SLAs.
• Implement reusable data product patterns: naming conventions, contracts, quality rules, SCD handling, and documentation “readmes.”
• Expose governed access for Power BI, APIs, and Copilot/agents; prevent direct system scraping.

ML & MLOps

• Stand up ML platform services: feature store, model registry, experiment tracking, inference endpoints, monitoring (drift, accuracy, latency), and A/B controls.
• Partner with Data Science to productionize models with SLOs and rollback runbooks.

Reliability, security, and FinOps

• Establish CI/CD for data and ML (branching, environments, approvals, blue/green).
• Implement observability: pipeline health, freshness, DQ, lineage coverage, cost dashboards, and alerting/On-Call.
• Enforce RBAC, secrets management, PII/HSE classifications, data retention, and DLP alignment with the Power Platform CoE.

Team & vendor leadership

• Manage a 3–5 FTE engineering team; recruit, coach, and develop talent.
• Direct vendor pods for ingestion, historian connectors, DataSphere modeling, and BI enablement; define DoD, SLAs, and knowledge transfer to insource sustainably.
• Collaborate with Data Governance/MDM, Reporting & BI, Automation, Cyber/IT, and plant controls teams.

Governance & quality

• Embed data quality rules, lineage, KPI canon, and MDM keys/survivorship into pipelines.
• Champion documentation, runbooks, post-incident reviews, and change control—especially for OT integrations.

Required qualifications:

• Minimum 7 years' in data/platform engineering with 3+ years leading teams or tech leads;
• Proven delivery of a Lakehouse or equivalent enterprise data platform and streaming/CDC pipelines.
• Strong hands-on with Azure data stack; or equivalent in AWS/GCP with ability to translate to Azure.
• Deep SQL and one major programming language (Python preferred); PySpark experience for large-scale transforms.
• Practical MLOps/DevOps: Git-based CI/CD, IaC, model registry/monitoring, containerized services, observability.
• Understanding of OT systems and historians, DCS/PLC data, OPC UA/MQTT patterns, and OT cybersecurity considerations (ISA-95/99).
• Working knowledge of SAP S/4HANA data structures and SAP DataSphere semantic modeling; ability to partner with SAP teams.
• Familiarity with Power BI consumption patterns (semantic models, RLS) and integration with certified datasets/APIs.
• Manufacturing or process industry experience strongly preferred.