About Mitra Innovation
At Mitra Innovation, we are the bridge between technical possibility and operational performance. Founded with a mission to empower businesses through intelligent technology, we are a trusted partner to over 50 strategic clients globally from ambitious scale-ups to global enterprises navigating complex digital transformations.
Project: Data Engineering & Modernization – (Client Based in USA)
JOB OVERVIEW
We are seeking a highly experienced, hands-on Lead / Principal Data Engineer with deep expertise in Snowflake, DBT, advanced SQL, and modern data engineering practices to support enterprise data modernization initiatives.
This role involves designing, developing, optimizing, and maintaining scalable data transformation pipelines, while modernizing existing data processing systems using Snowflake and DBT.
The successful candidate will be a technically strong, independent engineer capable of owning complex data engineering deliverables end-to-end, from understanding existing business logic and data architectures through implementation, testing, reconciliation, and production deployment.
This is primarily a hands-on engineering role (approximately 80% development), with additional responsibility for technical guidance, code reviews, architectural decisions, and mentoring other engineers. We are looking for someone who can go beyond implementing predefined requirements: an engineer who can identify problems, propose sound technical solutions, make informed implementation decisions, and deliver high-quality results with minimal supervision.
WHAT WE ARE LOOKING FOR
The ideal candidate is a highly capable, hands-on technical engineer who:
- 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.
- Is comfortable working in a fast-paced, distributed engineering environment with a strong emphasis on quality, accountability, and delivery.
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.
KEY RESPONSIBILITIES
1. Data Engineering & Development
- 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.
2. Data Migration & Modernisation
- 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.
3. Snowflake Engineering & Optimisation
- 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.
4. Technical Leadership & Ownership
- 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.
5. Engineering Quality & Modern Development Practices
- 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.
REQUIRED QUALIFICATIONS AND EXPERIENCE
Essential Technical Skills
Snowflake – Advanced Proficiency
- 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.
dbt – Advanced Proficiency
- 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.
- dbt Analytics Engineering Certification or dbt Architect Certification.
- 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.
SQL & Data Engineering
- 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.
Software Engineering & Delivery
- 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.
Professional Experience
- 12+ years of overall experience in data engineering, data warehousing, ETL/ELT, or related software engineering roles preferred.
- 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.
Preferred / Additional Skills
The following skills will be advantageous but are not mandatory:
- 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.
