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Lead Data Engineer (Client Side)

ICONMA, LLC

  • Blaine, MN
  • 30+ days ago
  • $11.13–$57.14 Per Hour

Highlights

Responsibilities: Design robust cloud data architectures using Fivetran, Data Build Tool and Snowflake to deliver scalable and maintainable platforms that support complex analytics and reporting needs across business domains. -Implement modular Data Build Tool models that transform raw datasets into curated, reusable and well documented data assets optimized for analytics performance and flexible self service consumption.

Numbers & Facts

LocationBlaine, MN
Salary$11.13–$57.14 Per Hour

Description

Our Client, an IT Services and Consultant company, is looking for a Lead Data Engineer (Client Side) for their Blaine, MN/ Hybrid location.
 
Responsibilities:
  • Design robust cloud data architectures using Fivetran, Data Build Tool and Snowflake to deliver scalable and maintainable platforms that support complex analytics and reporting needs across business domains. -Develop standardized patterns for data ingestion using Fivetran that ensure efficient extraction and loading from diverse source systems while maintaining consistency, reliability and clear operational observability. -Implement modular Data Build Tool models that transform raw datasets into curated, reusable and well documented data assets optimized for analytics performance and flexible self service consumption.
  • Optimize Snowflake databases by defining appropriate clustering strategies, storage configurations and query patterns that balance cost efficiency, resilience and high performance for varied workloads. -Establish comprehensive data modeling practices including naming conventions, layering strategies and documentation that promote clarity, reuse and long term sustainability of the data ecosystem.
  • Coordinate with product teams, analysts and engineers to translate analytical and reporting requirements into end to end data solutions covering ingestion, transformation, storage and consumption layers. -Define and implement data quality controls including validation rules, monitoring metrics and remediation workflows that improve trust in enterprise data and reduce downstream incidents. -Drive automation of deployment pipelines for Fivetran, Data Build Tool and Snowflake configurations using version control and continuous integration practices to enable consistent and reliable releases. -Partner with security and compliance stakeholders to embed robust access controls, encryption strategies and audit mechanisms within Snowflake and related components to protect sensitive information.
  • Provide technical guidance to engineering teams by reviewing solution designs, troubleshooting complex issues and sharing best practices to uplift capability and ensure architectural alignment. -Evaluate new platform capabilities, connectors and features in Fivetran, Data Build Tool and Snowflake, conducting structured experiments that validate value, performance and operational impact before adoption.
  • Collaborate with operations and support teams to establish proactive monitoring, alerting and runbooks that reduce downtime, accelerate incident resolution and improve platform stability.
  • Document reference architectures, design decisions and operational playbooks in clear and accessible formats that support onboarding, knowledge sharing and long term maintainability of the data landscape.
 
Requirements:
  • Possess a bachelors degree or equivalent experience in computer science information technology or a related field combined with at least twelve years of progressive work in data engineering or architecture.
  • Demonstrate strong proficiency in designing and operating Fivetran based ingestion pipelines including experience with connectors scheduling configurations and error handling practices. -show advanced hands on experience with Data Build Tool including project structuring modeling tests documentation and integration with modern version control workflows.
  • Bring deep practical knowledge of Snowflake features such as virtual warehouses time travel micro partitioning and query optimization for high volume and critical workloads. -apply solid understanding of data warehousing concepts star schemas data vault or similar modeling approaches and their application in large scale analytics platforms.
  • Utilize proven experience with cloud platforms such as AWS Azure or GCP focusing on networking storage identity management and integration with Snowflake based solutions. -display familiarity with modern data observability tools logging practices and performance monitoring frameworks relevant to cloud data environments.
  • Communicate complex architectural concepts clearly to technical and non technical audiences while influencing standards and encouraging adoption of best practices.
  • Years of experience: 16.00 Years of Experience
 
Why Should You Apply?

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