Software Engineering Manager - Enterprise Data platform

a { text-decoration: none; color: #464feb; } tr th, tr td { border: 1px solid #e6e6e6; } tr th { background-color: #f5f5f5; }

Short Description

We are seeking a highly technical Engineering Manager - Enterprise Data Platform to lead a team of engineers responsible for building and scaling a modern enterprise data platform. This role combines people leadership, platform architecture, and hands-on software engineering. The successful candidate will lead the development of a scalable data ingestion platform that collects data from manufacturing plants, enterprise applications, operational systems, IoT devices, databases, APIs, and streaming sources, transforming it into trusted, governed, and consumable data products within our enterprise data lake. This is a hands-on leadership role where the Engineering Manager is expected to actively contribute to technical design, software development, troubleshooting, and platform innovation while mentoring and growing a high-performing engineering team.

Description

We are looking for an experienced Engineering Manager - Enterprise Data Platform to lead the design, development, and operation of our next-generation data platform. As a technology leader, you will be responsible for managing a team of engineers while remaining deeply involved in architecture, solution design, and software development. You should be equally comfortable defining technical strategy, reviewing architecture, mentoring engineers, and stepping in as a Senior Developer when complex technical challenges arise. You will lead the development of a platform-first ecosystem that enables the efficient onboarding, processing, governance, and consumption of data from diverse enterprise and manufacturing environments. The platform will support analytics, reporting, Artificial Intelligence (AI), Machine Learning (ML), and operational decision-making across the organisation. You will work closely with Data Scientists, Machine Learning Engineers, Data Engineers, Product Managers, business stakeholders, and data consumers to ensure data is reliable, secure, governed, and accessible at scale. The ideal candidate combines strong engineering management capabilities with deep expertise in data platforms, distributed systems, cloud technologies, and modern AI-enabled engineering practices.

Responsibilities

Engineering Leadership & People Management

• Lead, mentor, and develop a team of software, platform, and data engineers.
• Drive engineering excellence through coaching, code reviews, technical guidance, and best practices.
• Manage hiring, onboarding, performance development, and career growth of team members.
• Foster a culture of ownership, innovation, collaboration, and continuous improvement.
• Define team goals and ensure alignment with business and platform objectives.
• Drive delivery excellence through effective prioritisation, planning, and execution.

Architecture & Platform Strategy

• Define and drive the technical vision, architecture, and roadmap for the Enterprise Data Platform.
• Design scalable, resilient, secure, and cost-effective platform capabilities.
• Establish architecture patterns for data ingestion, data access, metadata management, governance, and observability.
• Evaluate architectural trade-offs across batch, streaming, event-driven, and API-based solutions.
• Act as the technical decision-maker for critical platform initiatives.

Hands-On Engineering

• Actively contribute to software design, development, and code reviews.
• Step in as a senior developer or technical lead when required to solve complex engineering challenges.
• Develop and maintain production-grade software and platform services.
• Lead root cause analysis and implement long-term technical solutions.
• Ensure engineering teams follow modern software engineering principles, design patterns, and quality standards.
• Drive automation, standardisation, and platform reusability.

Data Ingestion Platform Development

• Build and scale a cloud-native data ingestion platform capable of collecting data from manufacturing plants, enterprise systems, applications, APIs, databases, IoT devices, external data providers, and streaming platforms.
• Design and implement ingestion patterns supporting batch, real-time streaming, and Change Data Capture (CDC).
• Develop reusable ingestion frameworks, connectors, SDKs, and self-service onboarding capabilities.
• Enable trusted movement of structured and unstructured data into enterprise data lake environments.
• Improve the reliability, scalability, and operational efficiency of ingestion services.

Data Governance, Quality & Observability

• Design and implement metadata management, lineage tracking, and governance frameworks.
• Establish enterprise-grade data quality validation and monitoring processes.
• Enable end-to-end visibility into data movement across the platform.
• Implement secure and policy-driven data access controls.
• Develop observability capabilities covering data freshness, reliability, usage, performance, and cost optimisation.

AI-Enabled Engineering & Platform Operations

• Leverage Generative AI and AI-assisted engineering tools to accelerate software development, troubleshooting, and operational excellence.
• Drive adoption of AI-powered solutions for identifying data ingestion failures, schema drift, data quality issues, pipeline bottlenecks, and platform anomalies.
• Partner with Machine Learning Engineers and Data Scientists to develop intelligent monitoring, anomaly detection, and automated remediation capabilities.
• Evaluate and implement AI/LLM-powered approaches for root cause analysis, incident investigation, operational insights, and developer productivity.
• Promote the effective use of AI-assisted coding, testing, observability, and platform support tools across engineering teams.

Collaboration & Stakeholder Management

• Collaborate closely with Data Scientists, Machine Learning Engineers, Data Engineers, Product Managers, and data consumers.
• Translate business and analytical requirements into scalable platform capabilities.
• Support AI, Machine Learning, analytics, and reporting initiatives through reliable and accessible data products.
• Influence platform strategy, engineering standards, and long-term technology decisions.
• Serve as a trusted technical partner across multiple cross-functional teams.

Qualifications

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related discipline.
• 10+ years of software engineering experience.
• 3+ years of experience leading or managing engineering teams.
• Strong experience building large-scale data platforms, distributed systems, or cloud-native applications.
• Proven experience designing and implementing enterprise data ingestion solutions.
• Deep expertise in batch, streaming, and Change Data Capture (CDC) architectures.
• Strong software development experience using Python, Java, or similar programming languages.
• Experience designing APIs, platform services, and scalable system architectures.
• Strong understanding of data governance, metadata management, lineage, data quality, and access control.
• Experience working closely with Data Scientists, Machine Learning Engineers, and analytics teams.
• Demonstrated ability to balance people leadership with hands-on technical contribution.

Preferred Qualifications

• Experience with Google Cloud Platform (GCP), including BigQuery, Dataflow, Dataproc, Pub/Sub, and Datastream.
• Experience with Airflow, Astronomer, or similar orchestration platforms.
• Experience with DataHub, OpenMetadata, Collibra, Alation, Apache Atlas, or similar governance solutions.
• Experience with Terraform, Infrastructure as Code (IaC), and GitHub Actions.
• Experience building enterprise data lakes and modern data platform ecosystems.
• Exposure to Artificial Intelligence, Large Language Models (LLMs), AI Agents, and intelligent automation frameworks.
• Experience implementing AI-powered observability, anomaly detection, and operational monitoring solutions.
• Experience building internal developer platforms, engineering productivity tools, or platform SDKs.

Who You Are

You are a builder and a leader. You enjoy creating scalable platforms while helping engineers grow and succeed. You are comfortable operating at multiple levels, from defining platform strategy and architecture to reviewing code and solving production challenges. You understand that successful data platforms are not just technology solutions but business enablers. You bring a product mindset to platform engineering and are passionate about delivering reliable, secure, and governed data capabilities that empower Data Scientists, Machine Learning Engineers, analysts, and business users to make better decisions and innovate faster. You embrace modern AI and Generative AI technologies and actively leverage them to improve engineering productivity, platform reliability, observability, and operational excellence.