The Role
Design, build, and maintain production-grade DBT data models and governed data domains on Azure Databricks. Develop scalable Delta Lake solutions using Unity Catalog and Medallion Architecture, implement data quality frameworks, and manage Terraform infrastructure. Collaborate through Git, pull requests, code reviews, and CI/CD workflows, leveraging AI-assisted development tools. Curate semantic data assets for self-service analytics, support modernization initiatives, establish engineering standards, and partner with stakeholders to deliver reliable data products.
Summary Generated by Built In
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.
Mandatory Requirements• 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.
• 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.
Preferred Requirements• 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.
• 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.
Skills Required
- 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 models, tests, macros, snapshots, project structure, and large-scale refactoring
- Advanced SQL skills and solid Python programming experience
- Hands-on experience with Azure Databricks, 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 using Terraform
- Experience with DBT testing frameworks, data quality validation, and automated quality controls
- Experience with dbt-expectations, Great Expectations, or similar testing frameworks
- Experience with Git-based workflows, pull requests, code reviews, and CI/CD pipelines
- Familiarity with 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
- Experience curating Databricks Genie environments or building semantic layers for natural-language analytics
- Experience in 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 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, 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
Encora Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Encora and has not been reviewed or approved by Encora.
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Healthcare Strength — Health coverage is described as employer-provided in multiple locations, with private plans and family coverage highlighted in Spain and Mexico. Medical insurance quality is presented as a recurring bright spot alongside standard coverage.
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Leave & Time Off Breadth — Time off includes paid holidays and PTO, with regional materials indicating additional leave provisions in certain countries. Leave is generally portrayed as conventional to generous depending on location.
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Flexible Benefits — Work-from-home flexibility is frequently highlighted as a plus, though it varies by role and client needs. Remote and hybrid options are positioned as part of the overall package.
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The Company
What We Do
Headquartered in Santa Clara, California, and backed by renowned private equity firms Advent International and Warburg Pincus, Encora is the preferred technology modernization and innovation partner to some of the world’s leading enterprise companies. It provides award-winning digital engineering services including Product Engineering & Development, Cloud Services, Quality Engineering, DevSecOps, Data & Analytics, Digital Experience, Cybersecurity, and AI & LLM Engineering. Encora's deep cluster vertical capabilities extend across diverse industries, including HiTech, Healthcare & Life Sciences, Retail & CPG, Energy & Utilities, Banking Financial Services & Insurance, Travel, Hospitality & Logistics, Telecom & Media, Automotive, and other specialized industries. With over 9,000 associates in 47+ offices and delivery centers across the U.S., Canada, Latin America, Europe, India, and Southeast Asia, Encora delivers nearshore agility to clients anywhere in the world, coupled with expertise at scale in India. Encora’s Cloud-first, Data-first, AI-first approach enables clients to create differentiated enterprise value through technology
