identifying capability gaps, implementing necessary tooling and processes, and promoting DataOps through leadership and user feedback initiatives. Requirements: Deploy and govern modern data stack technologies (e.g., Snowflake, Airflow, DBT, Fivetran, Airbyte, Tableau, Sisense, AWS, GitHub, Terraform, Docker) at enterprise scale for data engineering workloads. Develop deployable, reusable ETL/ELT solutions using Python, advanced SQL, and Jinja for data pipelines More ❯
Stack Cloud Data Warehouse - Snowflake AWS Data Solutions - Kinesis, SNS, SQS, S3, ECS, Lambda Data Governance & Quality - Collate & Monte Carlo Infrastructure as Code - Terraform Data Integration & Transformation - Python, DBT, Fivetran, Airflow CI/CD - Github Actions/Jenkins Business Intelligence - Looker Experience and Attributes we'd like to see Platform Engineering Expertise Extensive experience in platform engineering; designing, building, and More ❯
e.g. Databricks, Azure, AWS or GCP native stacks)Experience with platform observability and CI/CD for data platformsHands-on experience with modern data engineering tools such as dbt, Fivetran, Matillion or AirflowHistory of supporting pre-sales activities in a product or consultancy-based businessWhat Kubrick offers:We are a fast moving and fast growth business which is doing something More ❯
