About Our Client

Our client is a leading provider of cloud-based SaaS software for damage prevention, asset integrity, stakeholder engagement, and land management, helping energy, utility, telecom, and infrastructure companies protect their critical network infrastructure. With nearly three decades of industry experience, our client serves customers across North America and continues to expand its platform with new data-driven and AI-powered capabilities.

Role Overview

We're looking for an Engineering Manager to lead a new team building an AI-driven threat and risk management platform for pipeline asset integrity, bringing together a governed cross-product data platform, an AI-powered ingestion layer, and a reusable analytical layer under one roof. This is a player-coach role: you will spend real time in the codebase, in design reviews, and in code reviews, while also owning delivery and technical execution for a small, senior team spanning data architecture, data engineering, data science, application engineering, and QA. Domain knowledge in pipeline integrity or regulatory frameworks is a nice-to-have, not a requirement. What matters most is proven engineering leadership and hands-on technical depth.

Key Responsibilities

• Own delivery and technical execution against a phased delivery plan, including scope commitments, sequencing, dependencies, and release readiness at each gate.

• Run the team's operating rhythm (sprint planning, stand-ups, demos, retrospectives) with clear visibility into blockers and schedule risk; escalate risks early with a clear recommendation.

• Contribute directly to the codebase, particularly on foundational and high-risk components, and review pull requests, design documents, and architecture decision records.

• Prototype and de-risk unproven approaches, including agentic ingestion workflows and model execution patterns, before the team commits to them; debug production issues alongside the team.

• Partner with the Data Architect to establish and enforce standards for data modeling, lineage, governance, and platform patterns; own engineering quality, test coverage, CI/CD discipline, and observability.

• Ensure risk calculations, data transformations, and model outputs are traceable and reproducible to a standard that can withstand regulatory audit; hold the team accountable to security requirements (access control, tenant isolation, secrets management, SOC 2-aligned controls).

• Recruit, onboard, and develop a distributed team of mid-level and senior engineers; conduct one-on-ones, set expectations, and own performance management and career development.

• Partner daily with Product on requirements and scope tradeoffs, and interface regularly with other product teams to define data contracts for moving data into the new platform.

Requirements

• 8+ years of professional software or data engineering experience, including 2+ years of formally managing engineers.

• A track record of remaining hands-on as a manager, with recent, demonstrable individual contributions to production systems.

• Experience delivering data-intensive or ML-backed products end to end, from architecture through production operation.

• Working knowledge of modern cloud data platforms; Databricks and Azure experience strongly preferred, including Delta Lake, Unity Catalog, and workflow orchestration.

• Strong Python and SQL skills, with the ability to read, review, and write production code across the team's stack.

• Experience with CI/CD, infrastructure as code, environment promotion, and release management.

• Demonstrated ability to run a phased delivery plan with hard external commitments and communicate status honestly to executives.

• Experience hiring, onboarding, and developing engineers in a distributed or fully remote environment.

Nice-to-Have

• Experience leading teams that build regulated, audit-defensible software subject to external review.

• Familiarity with MLOps practices (model registries, versioning, deployment, monitoring, retraining pipelines).

• Experience with geospatial data and GIS-driven analytics.

• Exposure to agentic AI or LLM-based document extraction in production, beyond prototypes.

• Background in multi-tenant SaaS, including per-tenant isolation and data residency requirements.

• Experience using AI-assisted coding tools (Cursor, GitHub Copilot) or agentic coding tools (Claude Code), with a thoughtful perspective on responsible adoption across an engineering team.

• Experience standing up a new team on a new platform rather than inheriting a mature engineering organization.

• Understanding of pipeline integrity concepts (inline inspection, corrosion/crack growth, consequence modeling) or familiarity with PHMSA 49 CFR 192 / ASME B31.8S.

Benefits

• Join a dynamic, growing company with a strong reputation in its industry.

• Competitive salary.

• Comprehensive health plan options, including medical, dental, and vision coverage.

• 401(k) plan with company match.

• Flexible PTO policy plus company-paid holidays.

• Additional benefits, including life insurance, pet insurance, and employee discounts and perks programs.

• Generous one-time work-from-home stipend to help you set up your home workspace.

• Company events and opportunities to connect, including monthly team lunches, volunteer outings, and quarterly gatherings.

AI Use in Hiring

As part of our hiring process, this role may use artificial intelligence or automated tools to assist with reviewing and screening applications. These tools support, but do not replace, human judgment in making hiring decisions.

Your application will only be counted once you complete the full registration process on the KeyStone platform, including creating your profile, uploading your CV, and submitting your application. The AI interview is optional and encouraged, but is not required for your application to be counted.