Were looking for a talented Data Engineer to join our Data team and spread our power. The Data team develops and maintains the infrastructure for internal data and product analytics. We are looking for data enthusiasts, independent, logical thinkers with a can-do approach and a passion for problem-solving. We run a SaaS-based stack using BigQuery, Snowflake and dbt.
WHAT YOU'LL DO
Design, build, and maintain data pipelines, datasets and catalogs for fast-growing products and business groups.
Develop self-service data analytics solutions and infrastructure.
Support ad hoc needs and requests of internal stakeholders.
Collaborate with analysts, engineers, and internal customers from Product, Finance, Revenue, and Marketing.
WHAT YOU'LL DO
Design, build, and maintain data pipelines, datasets and catalogs for fast-growing products and business groups.
Develop self-service data analytics solutions and infrastructure.
Support ad hoc needs and requests of internal stakeholders.
Collaborate with analysts, engineers, and internal customers from Product, Finance, Revenue, and Marketing.
Requirements:
Bachelors or Masters degree in a relevant technical field, or equivalent hands-on experience in software development or DevOps.
3+ years of experience working as a Data Engineer, including end-to-end designing, orchestrating, and building cloud-based data pipelines (e.g., Airflow, Prefect, Dagster).
3+ years of experience with dimensional data modeling and data warehouse implementation, specifically MPP databases like BigQuery, Snowflake, and Redshift.
Strong knowledge of Python and Python-based data analysis tools such as Jupyter Notebooks and pandas.
Strong SQL writing skills. Ability to write highly performant queries.
Strong track record of executing projects independently in dynamic environments.
Fast understanding of data and business needs and ability to translate them into data models.
Team player with excellent communication skills.
Containerization (Docker): Essential for reproducible environments.
Knowledge of software engineering best practices: CI/CD concepts, code reviews, and unit testing.
ADVANTAGE
Production-level experience with dbt, including project design, transformation, testing, and documentation.
Infrastructure-as-Code (Terraform): Managing cloud resources (S3 buckets, IAM roles) via code.
CI/CD pipelines (GitHub Actions/Jenkins): Automating the testing and deployment of data models.
Bachelors or Masters degree in a relevant technical field, or equivalent hands-on experience in software development or DevOps.
3+ years of experience working as a Data Engineer, including end-to-end designing, orchestrating, and building cloud-based data pipelines (e.g., Airflow, Prefect, Dagster).
3+ years of experience with dimensional data modeling and data warehouse implementation, specifically MPP databases like BigQuery, Snowflake, and Redshift.
Strong knowledge of Python and Python-based data analysis tools such as Jupyter Notebooks and pandas.
Strong SQL writing skills. Ability to write highly performant queries.
Strong track record of executing projects independently in dynamic environments.
Fast understanding of data and business needs and ability to translate them into data models.
Team player with excellent communication skills.
Containerization (Docker): Essential for reproducible environments.
Knowledge of software engineering best practices: CI/CD concepts, code reviews, and unit testing.
ADVANTAGE
Production-level experience with dbt, including project design, transformation, testing, and documentation.
Infrastructure-as-Code (Terraform): Managing cloud resources (S3 buckets, IAM roles) via code.
CI/CD pipelines (GitHub Actions/Jenkins): Automating the testing and deployment of data models.
This position is open to all candidates.
