Engineering Manager – Data Platform

Role Overview

• Lead, coach, and develop Data Engineers through regular feedback, mentoring, and career development planning.
• Build a high-performing engineering culture focused on accountability, collaboration, innovation, and continuous improvement.
• Create clarity around expectations, goals, and team priorities.
• Support hiring, onboarding, and development of engineering talent across regions.
• Champion developer experience by improving tooling, workflows, documentation, and engineering productivity.
• Partner with Product Managers, Delivery Managers, QA, and Engineering Leadership to align priorities and ensure predictable delivery.
• Work closely with Lead Engineers to define technical scope, manage risks, remove blockers, and balance short-term delivery with long-term platform health.
• Ensure consistent adoption of SDLC practices, including code reviews, testing, CI/CD, release management, monitoring, and operational ownership.
• Advocate engineering best practices across architecture, quality, security, automation, observability, and reliability.
• Support engineers in making sound technical decisions and navigating trade-offs.
• Ensure stakeholders have clear visibility into progress, risks, and delivery outcomes.
• Partner with Lead Engineers to shape technical direction, architecture decisions, and engineering standards across the platform.
• Lead initiatives that improve developer experience, engineering productivity, platform reliability, and operational efficiency.
• Identify opportunities to leverage AI and emerging technologies to improve engineering workflows, platform capabilities, and business outcomes.
• Contribute hands-on to technical initiatives, proofs of concept, architecture spikes, and strategic engineering projects where appropriate.
• Foster a culture of technical curiosity, experimentation, and continuous learning.
• Support the evolution of the Data Platform through modern engineering practices, cloud-native technologies, and scalable architecture patterns.
• Champion experimentation and learning around emerging technologies as we aim towards becoming an AI-native organization.
• Drive improvements in onboarding, operational excellence, engineering consistency, and developer productivity across global teams.
• Partner with Support and Client Enablement teams to improve operational responsiveness and customer outcomes.
• Promote knowledge sharing and collaboration across teams, disciplines, and regions.

Requirements

• 7+ years of experience in data engineering
• 3+ years of experience leading, managing, or mentoring engineers in a product-focused environment.
• Strong track record as a hands-on data engineer before moving into leadership.
• Experience designing, building, and operating modern cloud-native data platforms, preferably in Azure.
• Strong understanding of modern software development practices, SDLC processes, and scalable platform architecture.
• Strong understanding of data engineering concepts, including data pipelines, orchestration, data quality, analytics platforms, and operational support.
• Experience with technologies such as Python, Databricks, Spark, Azure Data Factory, SQL, and CI/CD tooling.
• Ability to contribute technically through architecture reviews, technical design, code reviews, and hands-on development.
• Excellent stakeholder management and communication skills, with the ability to bridge technical and non-technical conversations.
• Passion for fostering innovation, improving developer experience, and enabling teams to deliver effectively at scale.
• Experience with DevOps practices, Infrastructure-as-Code, automated testing, and platform engineering concepts.
• Experience driving engineering productivity, developer experience, or platform modernization initiatives.
• Experience working in a fast-paced product or SaaS environment.

Tech Stack

• Azure
• Cloud
• Python
• SDLC
• Spark
• SQL