Lead / Principal Data Engineer - Snowflake & dbt

Mitra AI is seeking a hands-on Lead / Principal Data Engineer for a data engineering and modernization project supporting a client based in the USA. The role focuses on building and modernizing scalable Snowflake and dbt data transformation pipelines, owning complex engineering deliverables, and providing technical leadership.

Responsibilities:

• Design, develop, and maintain scalable, reliable, and high-performance data transformation pipelines using dbt and Snowflake
• Develop advanced dbt models, reusable macros, Jinja templates, incremental models, and modular transformation frameworks
• Design and implement data models across staging, intermediate, core, and reporting layers
• Build reusable data processing components supporting multiple clients, source systems, and business requirements
• Implement robust data transformation logic using complex SQL, CTEs, window functions, joins, aggregations, and analytical functions
• Apply data engineering best practices to ensure code maintainability, reusability, testability, and scalability
• Analyse and understand complex existing data processing systems, including legacy SQL-based ETL implementations
• Translate existing business rules and data processing logic into modern dbt and Snowflake implementations
• Independently investigate and resolve discrepancies between legacy and modernised data pipelines
• Develop reconciliation, validation, and regression testing approaches to ensure data accuracy and functional equivalence
• Identify opportunities to simplify and optimise existing processes without compromising business requirements
• Support the migration and modernisation of complex enterprise data platforms
• Develop and optimise complex Snowflake SQL transformations and analytical workloads
• Investigate query performance, execution plans, warehouse utilisation, and processing bottlenecks
• Apply Snowflake performance optimisation techniques, including query optimisation, incremental processing, and efficient data modelling
• Work with Snowflake objects, permissions, schemas, and environments as required for application development and delivery
• Troubleshoot data processing failures, transformation issues, and production performance problems
• Take end-to-end technical ownership of assigned workstreams and engineering deliverables
• Independently analyse complex requirements, design solutions, and drive implementation
• Provide technical guidance, mentoring, and support to other engineers within the offshore development team
• Perform code reviews, enforce engineering standards, and ensure consistent implementation quality
• Collaborate with data architects, technical leads, business analysts, and stakeholders to align solutions with architectural and business requirements
• Proactively identify technical risks, dependencies, and potential improvements
• Escalate strategic or architectural decisions appropriately while independently resolving routine engineering challenges
• Maintain high standards for code quality, automated testing, documentation, and production readiness
• Implement dbt tests, data quality checks, and reconciliation processes
• Work with Git/GitHub-based development workflows, including branching, pull requests, code reviews, and release management
• Support CI/CD practices and automated deployment of dbt projects across environments
• Use modern AI-assisted development tools, where appropriate, to accelerate engineering productivity while ensuring code correctness and quality
• Contribute to continuous improvement of data engineering processes, tooling, and technical standards

Requirements:

• Can independently understand complex existing data systems and translate them into modern Snowflake/dbt implementations
• Is equally comfortable designing a data architecture and personally implementing the underlying SQL and dbt models
• Can troubleshoot difficult data discrepancies, performance issues, and transformation problems without extensive supervision
• Produces clean, reusable, well-tested, and maintainable engineering solutions
• Takes ownership of technical outcomes rather than simply completing assigned development tasks
• Can provide practical technical leadership while remaining actively involved in development
• This position is best suited to an experienced engineer who combines deep Snowflake and dbt expertise with strong independent problem-solving skills, practical architecture experience, and a hands-on delivery mindset
• At least 5+ years of substantial hands-on Snowflake experience in production data engineering environments
• Strong understanding of Snowflake architecture, SQL processing, warehouse management, and performance optimization
• Experience developing, troubleshooting, and maintaining complex transformation pipelines on Snowflake
• Ability to independently diagnose performance, data quality, and processing issues
• At least 5+ years of hands-on dbt experience , preferably involving large-scale, production-grade projects
• Strong practical experience with dbt Core and/or dbt Cloud
• Advanced proficiency in dbt models, materialisations, incremental processing, testing, dependencies, and project configuration
• Strong experience developing reusable dbt macros, Jinja-based transformations, and modular dbt architectures
• Understanding of dbt development, deployment, testing, and documentation best practices
• Experience maintaining and improving complex dbt projects involving multiple data sources and interdependent models
• Expert-level SQL skills, including complex joins, CTEs, window functions, conditional logic, aggregations, and advanced data transformations
• Strong SQL debugging, query optimization, and data discrepancy investigation capabilities
• Experience designing and implementing dimensional, relational, and analytical data models
• Deep understanding of ETL/ELT architectures, enterprise data warehousing, and modern data engineering design patterns
• Experience working with large datasets and complex data transformation requirements
• Proven ability to investigate data mismatches, identify root causes, and implement reliable corrections
• Strong experience with Git/GitHub-based development, version control, branching strategies, and code reviews
• Experience working in structured development and release environments
• Ability to independently interpret requirements, design solutions, and deliver production-quality implementations
• Strong analytical reasoning, debugging, and problem-solving skills
• Proven experience delivering complex data engineering projects using Snowflake and dbt
• Previous experience as a Senior, Lead, Staff, or Principal Data Engineer, or equivalent hands-on technical leadership experience
• Demonstrated ability to take ownership of complex deliverables with minimal day-to-day supervision
• Experience guiding other engineers and contributing to technical design and implementation decisions
• Strong written and verbal communication skills, particularly when collaborating with distributed and offshore teams
• dbt Analytics Engineering Certification or dbt Architect Certification
• 12+ years of overall experience in data engineering, data warehousing, ETL/ELT, or related software engineering roles preferred
• Experience migrating legacy data pipelines from Microsoft SQL Server, PostgreSQL, SSIS, or other ETL platforms into Snowflake and dbt
• Experience with Python for data processing, automation, testing, and engineering utilities
• Familiarity with AWS services, particularly S3 and cloud-based data ingestion architectures
• Experience with dbt Fusion and modern dbt development environments
• Knowledge of CI/CD automation for dbt and Snowflake
• Experience with AI coding assistants or agents such as Claude Code, GitHub Copilot, or similar tools
• Familiarity with KNIME and other enterprise ETL tools
• Experience working with CRM, marketing, fundraising, direct mail, or multi-client data platforms
• Experience with reusable data models, metadata-driven transformations, and configurable client-specific processing
• Familiarity with JIRA and Agile delivery methodologies