Engineering Manager

Engineering Manager
Location: Remote (US)
Department: Insights (AI/ML)
Reports to: Director of Science and Architecture
About the Role
Irth is building a new AI-driven threat and risk management platform for pipeline asset integrity. The platform brings together three capabilities that have never previously lived in one place at Irth:
- A governed, cross-product data platform built on Databricks and Azure.
- An AI-powered ingestion layer that normalizes, repairs, and enriches customer data without services-heavy onboarding.
- A reusable analytical layer that runs industry-standard, Irth-developed, and customer-built risk models against that data.

We are looking for an Engineering Manager to lead the team building this platform.
You will own delivery and technical execution across a small, senior team spanning data architecture, data engineering, data science, application engineering, and QA. You will be accountable for what the team ships against a phased, gated delivery plan.
This is a player-coach role . You will spend a meaningful portion of each week in the codebase, reviewing designs, and participating in code reviews. We are not looking for someone who manages from a distance. We are looking for an experienced engineer who has grown into leading people and wants to continue doing both.
The work is highly regulated. Pipeline operators use these outputs to determine where to dig, what to repair, and how to justify that spending to regulators. Every calculation therefore needs to be traceable, reproducible, and defensible under audit.
If you enjoy solving problems where correctness genuinely matters, this is an opportunity to make a meaningful impact.
Key Responsibilities
1. Delivery Ownership — Primary Responsibility
- Own delivery and technical execution against the phase plan, including scope commitments, sequencing, dependencies, and release readiness at each gate.
- Run the team's operating rhythm, including sprint planning, stand-ups, demos, and retrospectives, with clear visibility into blockers and schedule risk.
- Translate product requirements into technical workstreams and break them into achievable increments.
- Hold the line on scope when delivery plans are at risk.
- Manage team capacity and velocity across parallel workstreams spanning the data platform, ingestion layer, and analytical layer.
- Escalate risks early and with a clear recommendation; surface milestone risk to leadership before it becomes a missed gate.

2. Hands-On Engineering
- Contribute directly to the codebase, particularly on foundational and high-risk components where experienced engineering involvement can materially improve outcomes.
- Review pull requests, design documents, and architecture decision records.
- Establish and maintain a high standard for engineering quality and technical decision-making.
- 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 rather than delegating them.

3. Technical Direction & Engineering Quality
- Partner with the Data Architect to establish and enforce standards for data modeling, lineage, governance, and platform patterns.
- Own engineering quality, including test coverage, CI/CD discipline, environment promotion, observability, and the definition of done.
- Make and document build-versus-buy and tooling decisions, including evaluation of third-party ingestion and AI tooling.
- Balance delivery pressure against technical debt and make those tradeoffs explicit.

4. Compliance, Security & Audit Readiness
- 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 platform security requirements, including access control, tenant isolation, secrets management, and SOC 2-aligned controls.
- Plan and schedule production-readiness acti