Data Engineer DBT

About the Role

We are looking for a skilled and passionate Data Engineer with deep expertise in dbt (data build tool) to help scale our modern data infrastructure. In this role, you will own the end-to-end data transformation layer, architect reliable ELT pipelines, and empower our analytics and product teams with clean, well-tested, and high-performance data models.

Key Responsibilities

• dbt Architecture & Development: Design, build, and maintain scalable dbt models, macros, snapshots, and packages to drive core business metrics and data transformations.
• Pipeline & Workflow Optimization: Own and optimize ELT workflows, collaborating with modern orchestration tools (e.g., Apache Airflow, Prefect, or Dagster) to ensure reliable data delivery.
• Data Quality & Governance: Implement automated data testing, data freshness checks, and documentation standards within dbt to guarantee high data reliability and traceability.
• Cloud Warehouse Management: Partner with stakeholders to optimize data warehouse performance (e.g., Snowflake, BigQuery, Databricks, or Redshift), focusing on query optimization, cost management, and compute sizing.
• CI/CD & Version Control: Evolve our CI/CD strategies for data codebases, managing git workflows, pull requests, and automated deployment pipelines for data models.
• Cross-Functional Collaboration: Act as a technical bridge between data analysts, data scientists, and software engineers to translate complex business logic into maintainable data pipelines.

Required Qualifications & Skills

• Experience:4+ years of professional experience in data engineering, analytics engineering, or software engineering.
• dbt Expertise: Proven, hands-on experience building, scaling, and maintaining production-grade dbt projects (Core or Cloud), including writing custom macros and utilizing packages.
• SQL & Data Modeling: Advanced SQL skills and a solid understanding of modern data modeling methodologies (Dimensional Modeling, Data Vault, or Star Schema).
• Cloud Data Warehouses: Strong working knowledge of cloud-native data warehouses like Snowflake, Google BigQuery, or Amazon Redshift..

Preferred / Nice-to-Have Skills

• Certifications: dbt Fundamentals / Advanced Certifications or Cloud Data Engineer certifications (e.g., Snowflake SnowPro, Google Cloud Professional Data Engineer).
• Data Observability: Experience with data quality and observability platforms (e.g., Monte Carlo, Great Expectations, Elementary).
• Infrastructure as Code (IaC): Exposure to tools like Terraform or Docker.