About the role
Senior Manager, Software Engineering who leads a high-performing engineering organisation building scalable, data-intensive platforms in the digital marketing and attribution space. Combines people leadership, delivery ownership, and architectural influence with deep hands-on credibility in big data, batch processing, and cloud-native distributed systems. Ideal for a leader who coaches engineers, owns predictable delivery, partners with product and platform leaders, and still shapes technical direction for attribution, forecasting, and batching pipelines at scale.
Responsibilities:
- Lead, mentor, and grow a team of data-platform engineers delivering critical platform capabilities.
- Own execution, delivery predictability, and technical quality for the team.
- Drive architectural decisions and engineering best practices for the data-processing systems.
- Partner with product managers to define roadmaps and translate business goals into technical plans.
- Foster a culture of ownership, accountability, and continuous improvement.
- Provide regular feedback, coaching, and career development guidance.
- Ensure systems meet standards for scalability, performance, security, and reliability.
- Improve engineering processes, CI/CD maturity, and operational excellence.
- Design, implement, and optimise data processing and batching solutions at scale.
- Build, schedule, and maintain DAG workflows and attribution/forecasting engines.
- End-to-end ownership: requirements definition, development, documentation, and stakeholder collaboration.
Requirements:
- 10+ years of software engineering experience.
- 3-4+ years managing and leading engineering teams.
- Strong system design, communication, and stakeholder management skills.
- Experience defining and tracking engineering metrics (delivery, quality, reliability).
- Ability to mentor junior and mid-level engineers and develop future technical leaders.
Core Technical Stack:
- Area: Technologies and Skills.
- Big Data: Scala, Apache Spark, Hadoop (HDFS, YARN, MapReduce).
- Languages: Scala (primary), Python (infrastructure modules).
- Cloud: AWS (primary), GCP as needed; cloud-native distributed systems.
- Data: SQL at TB scale, RDBMS/warehouse, Performance Optimisation, Operability.
- Workflows: DAG scheduling, batch pipeline orchestration.
- DevOps: Docker, Kubernetes, CI/CD pipelines, Git.
- Testing: Unit and integration test frameworks.
Nice to Have:
- Databricks Notebooks, Delta Lake, Catalogues.
- Observability and analytics: Tableau, Power BI, Grafana.
- Event-driven/streaming architectures.
- Experience in ad-tech / martech and large-scale data platforms.