About the role
Build the pipelines and data platforms behind our analytics and ML work. You will design streaming and batch pipelines, model warehouses and put data quality on rails.
You would join a squad of 6–10 engineers led by a principal engineer, working directly with client stakeholders. We run two-week sprints, trunk-based development and daily deploys.
Responsibilities
- Design and build ingestion pipelines with Kafka, Airflow and dbt
- Model data in Snowflake, BigQuery or Databricks for analytics and ML use cases
- Implement data quality checks, lineage and cataloguing
- Optimise pipeline performance and cloud cost
- Partner with data scientists to productionise features and models
- Document data contracts and semantic models for downstream teams
Required skills
- Strong SQL and Python
- Experience with a modern cloud warehouse or lakehouse
- Orchestration tooling (Airflow, Dagster or Prefect)
- Streaming or CDC experience (Kafka, Debezium)
- Dimensional and event-based data modelling
Preferred skills
- dbt
- Apache Spark or Flink
- Data quality frameworks (Great Expectations)
- ML feature stores and MLOps tooling
Qualifications
- Bachelor's degree in Computer Science, Engineering, Mathematics or related field
- 4+ years in data engineering roles
Technology stack
Python
SQL
Kafka
Airflow
dbt
Snowflake
BigQuery
Spark
Terraform
Benefits
- Competitive salary with annual review and performance bonus
- Extended health, dental and vision coverage from day one
- RRSP matching up to 5%
- 4 weeks paid vacation plus statutory holidays and a year-end shutdown
- Annual learning budget and paid certification exams (AWS, Azure, GCP, Kubernetes)
- Hybrid working with home-office stipend and modern equipment
- Dedicated time for open-source contribution and internal tech talks