Senior Data Engineer
- Design, build, and maintain robust, scalable data pipelines to power both analytics and product features.
- Contribute to the dbt analytics layer: establish best practices, implement core models, and ensure business metrics are consistent and trustworthy.
- Shape and maintain our data platform infrastructure (Terraform + Kubernetes), ensuring high availability, scalability, and observability.
- Lead the adoption of data quality, governance, and monitoring frameworks to build confidence in data.
- Mentor other engineers and contribute to raising the bar on engineering standards across the team.
- Partner with product managers and business stakeholders to turn data into insights and product opportunities.
- Collaborate with the engineering team to embed data flows into product architecture.
- Work alongside infra/platform engineers to design cloud-native, cost-efficient systems.
- Reduced breakages in data pipelines and the subsequent context switching.
- Be up to speed on our managed data infrastructure.
- Documented standards for data modeling, orchestration, and testing adopted by the team.
- 7+ years of professional experience in data engineering
- Deep expertise in SQL and Python, with proven experience building production-grade data pipelines.
- Hands-on experience with dbt in production environments, including modelling for analytics teams.
- Strong track record of working with infrastructure-as-code (Terraform) and container orchestration (Kubernetes).
- Familiarity with orchestration tools (Airflow, Dagster, Prefect).
- Ability to translate business problems into robust technical solutions.
- Data mesh concepts: decentralised ownership, domain-oriented pipelines, product-thinking with data.
- Streaming data systems (Kafka, Pulsar, Flink).
- Data observability, lineage, and governance tools.
- Experience scaling data teams in a startup environment.
- Passion for enabling a culture of self-serve analytics.
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