Senior Snowflake Data Engineer – Data Pipeline Modernization

Position: Senior Snowflake Data Engineer
Location: Remote
Duration: 6 Months
Start Date: ASAP – Ideally within 1–2 weeks
Engagement: Contract

Position Overview

We are looking for a Senior Snowflake Data Engineer to help modernize the client's data stack, reduce technical debt, and rapidly build scalable data pipelines into Snowflake.

The primary focus will be on data ingestion, pipeline development, transformation, and landing multiple source systems in Snowflake. The long-term objective is to make enterprise data accessible for Large Language Model (LLM) consumption through APIs, while ensuring the initial implementation stays aligned with the broader data modernization roadmap.

The ideal candidate is a hands-on data engineering expert who can move quickly, make sound technology decisions, and deliver measurable results within the first few weeks.

Key Responsibilities

• Design, build, and maintain data pipelines into Snowflake.
• Integrate multiple source systems and establish reliable data ingestion processes.
• Develop scalable and maintainable data ingestion and transformation workflows.
• Work with Snowflake, dbt, and Estuary as part of the existing data stack.
• Evaluate and recommend appropriate data ingestion technologies and architecture.
• Assess opportunities to leverage AWS-native services such as AWS Glue where appropriate.
• Transform and model data in Snowflake to support future analytical and AI/LLM use cases.
• Work closely with existing API infrastructure that exposes Snowflake data for LLM consumption.
• Help modernize the overall data stack and reduce accumulated technical debt.
• Ensure near-term pipeline development remains aligned with the long-term data architecture and roadmap.
• Collaborate with data transformation and architecture team members on service selection and implementation approach.
• Deliver tangible, production-ready results quickly and participate in executive-level "prove value" milestones.

Current Technical Environment

Primary Data Platform:

• Snowflake

Current Data Stack:

• Estuary → dbt → Snowflake

Potential Technologies:

• AWS Glue
• AWS-native data services
• REST/API integrations
• LLM/data access APIs

Existing Capability:

• Custom API layer already exists to expose Snowflake data for LLM consumption.

Required Skills

• Strong hands-on Snowflake experience
• Proven experience building data pipelines into Snowflake
• Strong data engineering and data warehouse experience
• Experience with ETL/ELT pipelines
• Strong SQL skills
• Experience with dbt
• Experience with data ingestion tools/platforms such as Estuary or similar
• Experience integrating multiple source systems into a centralized data warehouse
• Understanding of modern cloud data architecture
• Experience with AWS data services, preferably AWS Glue
• Ability to evaluate data engineering tools and recommend appropriate technical approaches
• Strong understanding of data transformation, modeling, and warehouse design
• Experience working with APIs and data exposed through APIs is preferred
• Understanding of AI/LLM data-access requirements is a plus

Preferred Qualifications

• Experience modernizing legacy or technically debt-heavy data environments.
• Experience with Snowflake + dbt implementations.
• Experience building data platforms intended to support AI/LLM applications.
• Experience designing data architectures that support future data modeling and analytics.
• Strong problem-solving and architecture skills.
• Ability to work independently and move quickly in an early-stage modernization effort.
• Strong communication skills and ability to collaborate with technical and executive stakeholders.

Project Goals

• Modernize the existing data stack.
• Reduce technical debt.
• Rapidly establish reliable data ingestion into Snowflake.
• Land multiple key source systems in Snowflake.
• Transform and prepare data for future modeling.
• Establish the foundation for LLM-accessible enterprise data.
• Ensure the implementation remains aligned with the long-term "M-state" roadmap.

Timeline & Success Measures

• Significant tangible results expected within the first 4 weeks.
• Initial source systems should be landed in Snowflake quickly.
• Multiple data sources targeted for ingestion and transformation by year-end.
• Initial kickoff targeted for next week or the following week.
• Progress will be measured against executive "prove value" milestones.

Ideal Candidate

The ideal candidate is a Senior/Lead-level Data Engineer with deep Snowflake expertise who has built data pipelines from source systems into Snowflake and understands modern ELT/data-platform architecture.

This is a hands-on role, not simply an architecture position. The candidate should be comfortable jumping in immediately, building pipelines, evaluating technology choices, and delivering production results quickly.

Must-Have: Snowflake + Data Pipeline Development + SQL + dbt + ETL/ELT + Cloud Data Engineering