Senior DWH Engineer / Data Architect

About this role

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.

Requirements:

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 meetingsand 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

Originally posted on Himalayas