Job Title: Senior / Lead Data Engineer Cloud (Terraform, DBT, Azure)
Key Skills: DBT, Azure Databricks, Terraform, SQL, Python, Delta Lake, Unity Catalog, AI-Assisted Development, Data Engineering, CI/CD
Experience: 5+ years of experience.
Location: Open to candidates across LATAM.
At Coforge, we are looking for a Senior / Lead Data Engineer Cloud (Terraform, DBT, Azure) (#22555) with the following profile.
Responsibilities
• Design, build, and maintain scalable DBT models on Azure Databricks, delivering curated and governed gold-layer data domains in a production environment.
• Develop and optimize data solutions using Delta Lake, Unity Catalog, and Medallion Architecture principles.
• Leverage AI-assisted development tools, including Claude Code and similar platforms, for model generation, refactoring, debugging, testing, and documentation.
• Build and maintain comprehensive data quality frameworks through DBT tests, validation rules, automated testing, and monitoring practices.
• Participate in rapid development and deployment cycles through pull-request-based workflows, AI-supported code reviews, and CI/CD quality gates.
• Collaborate with platform and engineering teams to define and implement AI-driven development practices and reusable engineering patterns.
• Curate datasets, semantic definitions, business terminology, and analytical assets to support natural-language data consumption and self-service analytics initiatives.
• Contribute to the evolution of data engineering best practices, including AI-assisted DBT development, data contracts, observability, and domain-driven ownership models.
• Support modernization initiatives through automation, migration acceleration, and AI-powered engineering workflows.
• Drive adoption of engineering standards, documentation practices, and scalable delivery methodologies.
• Measure and improve the effectiveness of AI-generated code, automated test coverage, and engineering productivity metrics.
• Work directly with business stakeholders to ensure delivered data products align with business objectives and quality expectations.
• Develop and optimize data solutions using Delta Lake, Unity Catalog, and Medallion Architecture principles.
• Leverage AI-assisted development tools, including Claude Code and similar platforms, for model generation, refactoring, debugging, testing, and documentation.
• Build and maintain comprehensive data quality frameworks through DBT tests, validation rules, automated testing, and monitoring practices.
• Participate in rapid development and deployment cycles through pull-request-based workflows, AI-supported code reviews, and CI/CD quality gates.
• Collaborate with platform and engineering teams to define and implement AI-driven development practices and reusable engineering patterns.
• Curate datasets, semantic definitions, business terminology, and analytical assets to support natural-language data consumption and self-service analytics initiatives.
• Contribute to the evolution of data engineering best practices, including AI-assisted DBT development, data contracts, observability, and domain-driven ownership models.
• Support modernization initiatives through automation, migration acceleration, and AI-powered engineering workflows.
• Drive adoption of engineering standards, documentation practices, and scalable delivery methodologies.
• Measure and improve the effectiveness of AI-generated code, automated test coverage, and engineering productivity metrics.
• Work directly with business stakeholders to ensure delivered data products align with business objectives and quality expectations.
Mandatory Requirements
• 5+ years of experience in Data Engineering.
• At least 2 years of hands-on experience building, deploying, and supporting production-grade DBT projects.
• Strong expertise in DBT, including models, tests, macros, snapshots, project structure, and large-scale refactoring.
• Advanced SQL skills and solid Python programming experience.
• Hands-on experience with Azure Databricks, including Delta Lake, Unity Catalog, and Medallion Architecture.
• Strong understanding of data modeling, data transformation, and cloud-based analytics platforms.
• Experience implementing and managing Infrastructure as Code (IaC) solutions using Terraform.
• Strong testing mindset, including DBT testing frameworks, data quality validation, and automated quality controls.
• Experience with tools such as dbt-expectations, Great Expectations, or similar testing frameworks.
• Experience working with Git-based development workflows, pull requests, code reviews, and CI/CD pipelines.
• Familiarity with modern AI coding tools such as Claude Code, GitHub Copilot, Cursor, or similar technologies.
• Ability to independently own and deliver data domains from design through production deployment.
• Strong communication and stakeholder engagement skills.
• At least 2 years of hands-on experience building, deploying, and supporting production-grade DBT projects.
• Strong expertise in DBT, including models, tests, macros, snapshots, project structure, and large-scale refactoring.
• Advanced SQL skills and solid Python programming experience.
• Hands-on experience with Azure Databricks, including Delta Lake, Unity Catalog, and Medallion Architecture.
• Strong understanding of data modeling, data transformation, and cloud-based analytics platforms.
• Experience implementing and managing Infrastructure as Code (IaC) solutions using Terraform.
• Strong testing mindset, including DBT testing frameworks, data quality validation, and automated quality controls.
• Experience with tools such as dbt-expectations, Great Expectations, or similar testing frameworks.
• Experience working with Git-based development workflows, pull requests, code reviews, and CI/CD pipelines.
• Familiarity with modern AI coding tools such as Claude Code, GitHub Copilot, Cursor, or similar technologies.
• Ability to independently own and deliver data domains from design through production deployment.
• Strong communication and stakeholder engagement skills.
Preferred Requirements
• Experience curating Databricks Genie environments or building semantic layers for natural-language analytics.
• Experience within insurance, financial services, or other regulated industries.
• Knowledge of policy, claims, customer, exposure, or regulatory reporting data domains.
• Experience with PySpark for large-scale transformation workloads.
• Experience with orchestration technologies such as Databricks Workflows or Apache Airflow.
• DBT, Databricks, Azure, or cloud-related certifications.
• Experience with Data Observability, Data Contracts, and Domain Ownership frameworks.
• Exposure to Databricks DLT (Delta Live Tables), Lakeflow, Asset Bundles, and advanced Databricks capabilities.
• Experience implementing AI-assisted migration, modernization, or engineering transformation initiatives.
• Familiarity with engineering productivity metrics and AI adoption measurement frameworks.
• Experience within insurance, financial services, or other regulated industries.
• Knowledge of policy, claims, customer, exposure, or regulatory reporting data domains.
• Experience with PySpark for large-scale transformation workloads.
• Experience with orchestration technologies such as Databricks Workflows or Apache Airflow.
• DBT, Databricks, Azure, or cloud-related certifications.
• Experience with Data Observability, Data Contracts, and Domain Ownership frameworks.
• Exposure to Databricks DLT (Delta Live Tables), Lakeflow, Asset Bundles, and advanced Databricks capabilities.
• Experience implementing AI-assisted migration, modernization, or engineering transformation initiatives.
• Familiarity with engineering productivity metrics and AI adoption measurement frameworks.
Published on: 31-08-2026
At Coforge, we hire professionals solely based on their skills and qualifications and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.
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