Job Title

Tredence is a leading data science and analytics consulting company known for solving complex business problems with a strong focus on “Last-Mile Adoption.” We combine deep domain expertise with advanced engineering to deliver scalable and business-impactful data solutions.Role Overview We are looking for an experiencedDBT Engineerto join our Data Engineering team. The ideal candidate has a strong data engineering background, hands-on experience building scalable data transformation pipelines usingDBT (Data Build Tool) , and a solid understanding of cloud data platforms. You will work closely with analytics, product, and consulting teams to design and operationalize high‑quality data models that power analytics and AI solutions for global clients.Key Responsibilities Design, build, and optimizeDBT-based data transformation pipelinesfollowing modular, testable, and scalable development patterns. Develop and maintainsemantic data models , ensuring data quality, governance, and version control. Collaborate with cross-functional teams to understand data needs and convert business requirements into technical implementations. Integrate DBT workflows with orchestration tools such asAirflow ,Astronomer ,Dataform , or cloud-native schedulers. Implementdata quality tests , documentation, and lineage using DBT features. Optimize performance of transformations and queries on cloud data warehouses likeSnowflake, BigQuery, Redshift, or Azure Synapse . Work with CI/CD pipelines for automated DBT deployments using Git, GitHub Actions, Azure DevOps, or similar tools. Participate in code reviews, enforce engineering best practices, and contribute to improving internal frameworks and accelerators. Troubleshoot data issues and provide ongoing support for production data pipelines.Required Skills & Experience 5–9 yearsof experience in Data Engineering with at least2+ years hands-on in DBTdevelopment. Strong SQL skills and experience modeling data using methodologies likeKimball ,Data Vault , orstar/snowflake schemadesign. Expertise in at least one major cloud platform: Azure (preferred) , AWS, or GCP Hands-on experience with cloud data warehouses: Snowflake, BigQuery, Redshift, Databricks SQL, Synapse, etc. Solid understanding of software engineering practices such as version control, CI/CD, modular code design, and testing. Experience with orchestration tools (Airflow, Prefect, Dagster, Azure Data Factory, etc.). Familiarity with Python for scripting and automation. Strong problem-solving skills with the ability to work in a fast-paced consulting environment.