Senior Data Engineer (Snowflake, dbt, Fivetran)

Role Overview: We are seeking a Senior Data Engineer who is highly hands-on and experienced in building modern, scalable data pipelines and transformation frameworks using Snowflake and dbt. The ideal candidate brings deep, practical experience in dbt coding, Snowflake engineering, and Fivetran, along with a strong sense of accountability and the ability to operate independently.

Job Title : Senior Data Engineer (Snowflake, dbt, Fivetran)
Location: McLean, VA – 100% Onsite from Day 1
Duration: 6 Months


Salary Range: $70.00 - $72.00/Hour on W2 (Without Benefits).
Applicants must be willing to work on W2.
 
Role Overview:

• We are seeking a Senior Data Engineer who is highly hands-on and experienced in building modern, scalable data pipelines and transformation frameworks using Snowflake and dbt.
• This role focuses on delivering high-quality, production-grade data solutions with strong engineering discipline, leveraging Python, CI/CD, and Git-based development practices.
• The ideal candidate brings deep, practical experience in dbt coding, Snowflake engineering, and Fivetran, along with a strong sense of accountability and the ability to operate independently.
• Direct, hands-on experience with Fivetran, dbt, and Snowflake is a mandatory screening requirement. Candidates without strong hands-on experience across all three will not be considered.

Key Responsibilities

• Design, build, and maintain scalable ELT pipelines, leveraging Fivetran for ingestion and dbt for transformation on Snowflake.
• Develop and maintain robust dbt projects, including:
  • Modular models (staging, intermediate, marts).
  • Reusable macros and Jinja templating.
  • Snapshots for SCD Type 2 handling.
  • Schema and custom data quality tests.
  • Documentation using dbt docs.
• Implement modular and reusable dbt architecture supporting multi-environment deployments (dev, test, prod).
• Design and implement scalable data models using best practices (dimensional modeling, star schema, and data vault where applicable).
• Optimize Snowflake performance and cost efficiency, including:
  • Query tuning and execution optimization.
  • Warehouse sizing and workload management.
  • Effective use of micro-partitions, clustering, and pruning.
• Build and enforce strong data quality and validation frameworks, including:
  • Unit testing for transformations (dbt and custom frameworks).
  • Data reconciliation and consistency checks.
• Develop Python-based solutions for automation, orchestration support, metadata-driven processing, and operational tooling.
• Implement and enforce Git-based development practices:
  • Version control, branching strategies, pull requests, and code reviews.
  • Consistent and collaborative engineering workflows.
• Build, maintain, and enhance CI/CD pipelines for dbt deployments:
  • Automated build, test, and deployment processes.
  • Environment promotion (dev → test → prod).
  • Integration with enterprise deployment pipelines.
• Work with orchestration tools such as Airflow / Astronomer to schedule, monitor, and manage data pipeline execution (preferred).
• Collaborate closely with platform, governance, and business teams to align on data requirements, access control, and delivery expectations.

Required Qualifications

• 7+ years of experience in data engineering / analytics engineering roles.
• Strong hands-on experience with dbt in production, including:
  • Model development and dependency management.
  • Macro development and reusable frameworks.
  • Testing strategies (schema tests, custom tests).
  • Deployment and environment management.
• Strong Snowflake expertise, including:
  • Data modeling and warehouse design.
  • Performance tuning and cost optimization.
  • Deep understanding of virtual warehouses, micro-partitions, clustering, and query pruning.
  • Role-based access control (RBAC) and secure data access.
• Advanced SQL expertise with ability to build and optimize complex transformations.
• Strong Python programming skills for data engineering use cases.
• Proven experience with Git integration, including collaborative development workflows.
• Strong experience implementing CI/CD pipelines for data platforms and dbt deployments.
• Experience building and maintaining production-grade data pipelines with SLAs, monitoring, and reliability standards.
• Mandatory requirement: Direct, hands-on experience with Fivetran, dbt, and Snowflake in production, including Fivetran connector setup and troubleshooting, dbt model development and testing, and Snowflake data engineering, performance tuning, and secure access patterns.

Preferred Qualifications

• Experience with Airflow / Astronomer or similar orchestration tools.
• Exposure to data governance, lineage, and observability tools.
• Financial services / banking domain experience is strongly preferred, though not mandatory.

Key Behavioral Expectations

• Demonstrates strong accountability, taking full responsibility for deliverables and outcomes end-to-end.
• Highly self-motivated and proactive, able to operate independently and drive work forward with minimal oversight.
• A continuous learner who stays current with evolving data technologies and applies best practices effectively.
• Takes initiative to identify issues, improve systems, and deliver scalable, high-quality solutions in a fast-paced environment.

What Success Looks Like

• Delivers well-structured, scalable, and reusable dbt models with strong testing and documentation.
• Builds Snowflake solutions that are high-performing, cost-efficient, and production-ready.
• Implements robust CI/CD and Git-driven engineering practices ensuring reliable deployments.
• Develops pipelines that are well-orchestrated, monitored, and governed.
• Consistently operates with high accountability and delivers quality outcomes at scale.

Core Technology Stack

• Astronomer (Apache Airflow) for pipeline orchestration and monitoring.
• Fivetran for ingestion and data exports.
• dbt for transformations, testing, and documentation.
• Snowflake as the cloud data platform.
• Python and PySpark for advanced transformations alongside dbt.
• The role is expected to deliver hands-on production work across this stack.

Experience Required:

• Overall Experience Required is 4–6 years.
• Must have a 7+ years of experience in Google Data Engineering.