About the role
The candidate Will have responsibilities across the following functions:
Team Leadership:
- Lead and mentor a growing team of data engineers, including talent reviews and career development.
- Own planning, estimation, prioritisation, and delivery tracking that aligns with leadership direction and expectations.
- Coordinate intake and stakeholder communication for data requests and roadmap planning.
- Set and enforce compliance with architecture standards, engineering standards for code quality, testing, documentation, and production readiness.
- Foster a culture of curiosity and continuous learning, where engineers explore new technologies, share knowledge, and question assumptions.
Data Pipelines and Integration:
- Design, build, and maintain ETL/ELT pipelines from enterprise applications, internal services, and third-party APIs.
- Design, develop, and operationalise robust and scalable data pipelines from enterprise applications, internal services, and third-party APIs that support business needs.
- Lead in designing and building production data pipelines from data ingestion to consumption using GCP services, Python, BigQuery, DBT, SQL, Apache Airflow, Celigo, etc.
- Drive AI adoption across the team's engineering workflows - the team has a mandate for AI adoption, and you'll be expected to be a role model, to champion, remove friction, and help.
Reliability and DataOps:
- Build monitoring and alerting for data jobs, orchestration, and lakehouse health.
- Participate in and evolve production support, incident response, and on-call rotations.
Strategic Leadership:
- Translate business goals into scalable data and automation solutions in partnership with both business and technology stakeholders.
- Champion data democratisation and self-service access to data and analytics across the company.
- Evaluate and recommend tools and end-to-end solutions across Analytics, Data Engineering, ML Engineering and Data Governance.
- Apply systems thinking to identify underlying problems and/or opportunities.
Requirements:
- 5+ years of hands-on data engineering experience in cloud environments.
- Direct experience in design and development of large scale data solutions using GCP services like DataProc, Dataflow, Cloud Bigtable, BigQuery, Cloud SQL, Pub/Sub, Cloud Data Fusion, Cloud Composer, Cloud Functions, Cloud Storage, Compute Engine, Looker and Cloud IAM.
- Experience in implementing cloud data solutions in the context of business applications, cost optimisation, business strategic needs and future growth goals as it relates to becoming a data-driven organisation.
- Expert-level knowledge of architecture frameworks, methodologies, and tools.
- Solid working understanding across all the disciplines within a data team - Data Visualisation, Data Governance, Artificial Intelligence and Machine Learning.
- Experience implementing Infrastructure as Code (IaC), including automating Cloud IAM and Data Policy Tags.
- Excellent communication skills and the ability to articulate technical concepts to non-technical stakeholders.
- Expert-level knowledge of Data Modelling.
- Strong execution habits: you create and maintain project timelines, know when things are off track before your team tells you.
- A proactive mindset toward AI-assisted engineering - you should already be using AI tools (Copilot, Claude, ChatGPT, or similar) in your own work and have opinions about how they change engineering workflows, code quality, and team productivity.
- We're looking for someone who sees
- AI as a multiplier.