Senior Engineering Manager, Data & Cloud Platform

Mitratech

1d•$190,000 - $210,000•Remote

About The Position

Mitratech is seeking a Senior Engineering Manager to lead the Data & Cloud Platform function for its AI organization. This role is responsible for the data and infrastructure foundation that enables higher-level AI platform capabilities and product experiences. The position involves a blend of cloud platform engineering and data responsibilities, including building a unified access layer across fragmented product data stores, shaping shared agent memory and context systems, operating the cloud foundations, and executing the broader data strategy. This is a hands-on leadership role, with the expectation of spending approximately half the time writing and reviewing production code, especially as the team, which starts small (one to two direct reports), scales.

Requirements

8+ years of software engineering experience including meaningful experience leading engineers as a manager or technical lead.
Built and operated shared platform and data systems in production across multiple teams.
Strong experience with cloud infrastructure, distributed systems, developer platforms, or internal platform engineering.
Hands-on experience building or operating shared data systems: ETL/ELT pipelines, internal data services, warehouse or lakehouse environments, or other multi-team data platforms.
Experience designing platform APIs or integration layers.
Demonstrated exposure to production AI systems, especially where data architecture and infrastructure materially affect quality, reliability, latency, or cost.
Comfort operating in ambiguous environments where the architecture is evolving.
A strong bias toward hands-on execution, systems thinking, and building scalable foundations that other teams can trust.
Experience with agent memory, retrieval systems, semantic search, or data architectures optimized for LLM-powered applications.

Nice To Haves

Experience in multi-cloud or hybrid-cloud environments.
Experience in legal technology, enterprise SaaS, or regulated environments where access controls, auditability, and customer-specific constraints shape technical design.

Responsibilities

Own the cloud and data foundation for the AI organization — shared runtime infrastructure, CI/CD patterns, and platform services used by AI teams.
Design and build a unified data access layer for agents and AI features.
Define where shared AI data should live — agent memory, context stores, and shared service datasets.
Build and operate data ingestion and transformation pipelines that support AI workloads and analytics.
Shape the technical execution of the organization’s data strategy, especially around scale, operability, cost, and long-term maintainability.
Partner with the AI Quality & Governance team to support privacy-sensitive data handling, PII-aware workflows, and safe access patterns.
Provide platform patterns, reference architectures, and reusable services.
Hire, mentor, and grow the team over time.