Senior DWH Engineer / Data Architect

Recruiting; United States; On-site; USD 83,000 - 124,000; Part time

Job summary

Recruiting is building a greenfield Data Platform that consolidates data from third-party SaaS products into centralized analytical environments like Snowflake, Databricks, and Microsoft Fabric. You will define architecture, build ETL/ELT pipelines, and establish the foundation for a long-term data platform offering.

The role requires 5 years of experience in data engineering or data architecture, strong SQL, and upper‑intermediate English.

Qualifications

• 5 years of experience in Data Engineering or Data Architecture.
• Strong commercial experience with Snowflake and/or Databricks or Microsoft Fabric.
• Experience designing and building Data Warehouse or Data Lake solutions.
• Hands-on experience with Big Data processing.
• Strong ETL/ELT pipeline development experience.
• Excellent SQL skills.
• Experience working with large‑scale datasets (terabytes of data).
• Strong understanding of data modeling and performance optimization.
• Upper-Intermediate English.
• Ability to work independently and make architecture decisions.

Responsibilities

• Design and implement scalable Data Warehouse and Data Lake architectures.
• Build and optimize ETL/ELT pipelines.
• Develop data ingestion solutions for multiple third-party SaaS platforms.
• Process and transform large volumes of structured data.
• Design incremental data synchronization processes.
• Evaluate and introduce modern Data Engineering tools and technologies.
• Optimize platform performance, scalability, and cost efficiency.
• Collaborate with stakeholders to define the future architecture of the platform.
• Ensure high-quality, analytics‑ready data for reporting and downstream consumer.

Skills

Data engineering

SQL

Big data processing

Data modeling

Architecture decision making

English (Upper-Intermediate)

Tools

Snowflake

Databricks

Microsoft Fabric

Job description

About the project
We are building a new Data Platform that consolidates data from various third-party SaaS products into centralized analytical environments such as Snowflake, Databricks, and Microsoft Fabric. The platform will provide clients with secure access to reporting and analytics-ready data through Data Warehouses and APIs.
This is a greenfield initiative where the selected engineer will play a key role in defining architecture, selecting technologies, building ETL/ELT pipelines, and establishing the foundation for a long-term Data Platform offering.

• 5 years of experience in Data Engineering or Data Architecture

• Strong commercial experience with Snowflake and/or Databricks or Microsoft Fabric

• Experience designing and building Data Warehouse or Data Lake solutions

• Hands-on experience with Big Data processing

• Strong ETL/ELT pipeline development experience

• Excellent SQL skills

• Experience working with large‑scale datasets (terabytes of data)

• Strong understanding of data modeling and performance optimization

• Upper-Intermediate English

• Ability to work independently and make architecture decisions

Responsibilities:

• Design and implement scalable Data Warehouse and Data Lake architectures

• Build and optimize ETL/ELT pipelines

• Develop data ingestion solutions for multiple third-party SaaS platforms

• Process and transform large volumes of structured data

• Design incremental data synchronization processes

• Evaluate and introduce modern Data Engineering tools and technologies

• Optimize platform performance, scalability, and cost efficiency

• Collaborate with stakeholders to define the future architecture of the platform

• Ensure high-quality, analytics‑ready data for reporting and downstream consumer

Schedule: Flexible Part-Time (approx. 20–25 hours per week). Agile schedule with the ability to balance daily workload (4–6 hours/day).No late meetings and overtime.

Why Choose Us? What Do We Offer that’s Exceptional?

• Greenfield architecture with no legacy constraints

• Opportunity to build a modern Data Platform from scratch

• Freedom to influence technology choices and architectural decisions

• Challenging Big Data and distributed data processing problems

• High level of ownership and autonomy

• Direct impact on a new strategic company initiative

• Work with the latest technologies in the Data Engineering ecosystem