Data Engineer

Qualifications & Experience

• 2 to 3 years of progressive experience in Data Engineering or Analytics Engineering.
  

• Prior experience in a scale-up, product-led, or data-centric organization.
  

• Familiarity with BI tools is a plus (Tableau, PowerBI, Looker), though not a core requirement.
  

• Proven track record of building and managing dbt models in a production environment.
  

• Experience with API-based ingestion (using Airbyte, dlt, and/or custom scripts) is a plus.
  

• Ability to work effectively with people at all levels in an organization.
  

• Data Pipeline Execution: Build and maintain reliable data pipelines that automate ingestion from both structured and unstructured sources. Leverage Python and SQL to ensure data flows are secure and traceable.
  

• Transformation Layer: Develop and manage transformation workflows using dbt, ensuring data models are modular, tested, and version-controlled via Git.
  

• Orchestration & Scheduling: Schedule data workflows using tools such as Airflow, Prefect, or GCP Workflows, ensuring automated and timely data delivery across our systems.
  

• Cloud Warehouse Support: Maintain the data warehouse environment (BigQuery), focusing on query performance, cost monitoring, and schema organization.
  

• Observability & Quality: Implement data validation tests and lineage tracking using frameworks like Elementary to ensure high levels of data integrity and trust.
  

• Infrastructure as Code (IaC): Assist in managing and deploying cloud resources (BigQuery datasets, IAM roles, GCS buckets) using Terraform to ensure a reproducible and documented environment.
  

• Version Control & CI/CD: Maintain the integrity of our codebase using GitHub. Ensure that every dbt change or Python script follows our CI/CD patterns (GitHub Actions) for automated testing and deployment.
  

• Analytics Support: Collaborate with BI analysts and product teams to provide clean, optimized data sets for reporting and internal tools.
  

• Operational Documentation: Maintain clear documentation (dbt docs/SOPs) for pipelines and models to support team-wide data discovery and "self-service" BI.