Snowflake Data Engineer

EXL•New York, NY

About The Position

We are looking for an experienced, hands-on Data Engineer to build and support a modern data platform using Fivetran, dbt, Snowflake, and cloud data engineering practices. The role focuses on scalable data ingestion and transformation pipelines, data quality and orchestration patterns, and enterprise data products used by analytics and business teams. The engineer will own and troubleshoot the complete data lifecycle from source systems through Fivetran and Snowflake to dbt. The position also uses Python for data engineering utilities and encourages AI-assisted development with tools such as Claude, Cursor, and VS Code AI assistants. Deep AI/LLM development experience and advanced Python development are not required. The posting also lists responsibilities for managing data modelling projects, ensuring adherence to policies, providing strategic guidance, and leading a team. It calls for a graduate in Computer Science, Data Science, or a related field, and 4–6 years of experience in data modelling or data analysis.

Requirements

Strong hands-on experience with Snowflake, including advanced SQL, data modeling, performance optimization, RBAC, stages, and data integration patterns.
Strong hands-on experience with dbt or dbt Cloud, including models, tests, macros, incremental models, sources, documentation, jobs, and deployment patterns.
Strong hands-on experience with Fivetran, including connector configuration, schema management, sync troubleshooting, monitoring, and ingestion architecture.
Strong SQL skills, including complex joins, CTEs, window functions, reconciliation, and query optimization.
Experience with Git and CI/CD, including branching, pull requests, code reviews, and automated deployment workflows.
Working knowledge of Python for data processing, automation, API integrations, file processing, data validation, and operational scripting; ability to read, troubleshoot, and develop Python-based data engineering utilities.
Familiarity with Claude and AI-assisted development workflows; comfort using tools such as Claude, Cursor, or VS Code AI-assisted development capabilities.
Production experience implementing and supporting Fivetran pipelines.
Experience operating within enterprise DEV → QA → PROD SDLC and change-management processes.
Experience designing reusable ingestion and transformation patterns.
Strong production troubleshooting and root-cause analysis skills.
Graduate in Computer Science, Data Science, or a related field.
4–6 years of experience in data modelling or data analysis.

Nice To Haves

Apache Airflow or AWS MWAA for orchestration.
AWS services such as S3, IAM, Lambda, Secrets Manager, and FSx.
Salesforce, Marketo, and other SaaS integrations.
REST API and SFTP/file-based ingestion.
Snowflake Secure Data Sharing.
Snowpark or Snowpark Container Services (SPCS).
Databricks.
Terraform or Infrastructure as Code.
Data quality and observability frameworks.
Experience modernizing or migrating legacy ETL/integration platforms.
5+ years of Data Engineering or related experience.
2+ years of hands-on experience with Snowflake and dbt.

Responsibilities

Design, build, and support end-to-end data pipelines using Fivetran, dbt, and Snowflake.
Configure and manage Fivetran connectors across SaaS applications, databases, APIs, and file-based sources.
Develop and maintain dbt staging, intermediate, mart, and publish models.
Implement dbt tests, source freshness checks, incremental processing, documentation, and reusable macros.
Develop optimized Snowflake SQL and scalable analytical data models.
Support Snowflake objects, security and RBAC, stages, data sharing, and data-loading patterns.
Implement data quality, reconciliation, monitoring, and operational alerting.
Troubleshoot production pipelines and perform root-cause analysis across ingestion and transformation layers.
Participate in Git-based development, CI/CD, code reviews, and DEV/QA/PROD deployments.
Collaborate with application, infrastructure, security, analytics, and business teams to onboard data sources.
Manage data modelling projects.
Ensure adherence to policies.
Provide strategic guidance.
Lead a team.
Use AI-assisted development tools for coding, troubleshooting, code review, refactoring, debugging, test generation, understanding codebases, documentation, and development productivity while following enterprise security and development standards.