Mgr Software Engineering

About the role

This role leads to the Analytics Engineering function with a focus on embedding AI into delivery while ensuring strong InfoSec and data governance standards. It sits at the intersection of engineering, AI, and risk—driving practical adoption of AI in analytics workflows without compromising compliance or control.

Responsibilities

• Team Leadership & Delivery: Lead and support analytics engineers, ensuring clear priorities and consistent delivery. Remove blockers, drive accountability, and maintain pace across initiatives.
• AI Adoption in Analytics: Identify and prioritize high-impact AI use cases (e.g. LLM-driven parsing, enrichment, copilots). Embed AI into pipelines and workflows—not as experiments, but as part of production delivery. Guide teams on practical implementation and evaluation of AI outputs.
• InfoSec & Governance: Ensure all AI and data usage aligns with internal InfoSec, privacy, and regulatory requirements. Define and enforce standards for safe AI usage (e.g. PII handling, approved models, access control). Work closely with InfoSec and architecture teams on risk management and approvals.
• Data Quality & Control: Establish validation frameworks for AI outputs (e.g. Benchmarking, HITL, drift monitoring). Maintain strong data governance, lineage, and quality across analytics assets.
• Stakeholder Engagement: Partner with product, business, and platform teams to align priorities and use cases. Translating business needs into scalable analytics and AI solutions.

Requirements

• Bachelor’s Degree (Engineering/Computer Science preferred but not required); or equivalent experience required. Advanced degree preferred
• Proven experience in Data and Scoring Engineering, data management and data strategy experience with technical knowledge along with management experience.
• Strong background in analytics engineering / data engineering (SQL, data modelling, modern data platforms)
• Experience applying AI/LLMs in production use cases, not just experimentation
• Solid understanding of data governance, InfoSec, and regulatory considerations (PII, model risk, access control)
• Proven experience leading teams and driving delivery in a fast-paced environment
• Ability to balance innovation with control—moving quickly without introducing risk
• Strong communication skills across both technical and business stakeholders

Nice to have

• Experience with platforms such as Databricks, Synapse, Power BI
• Familiarity with agent-based workflows, prompt engineering, and evaluation frameworks
• Exposure to regulated environments or sensitive data domains