Data Engineer (Data Warehouse Engineer)
Langate ·
- Work mode
- Remote
- Employment
- Part time
- Category
- Data engineering
- Experience
- 5+ years
- Visa sponsorship
- No
- Company size
- 51-200
Langate ·
ATS Client · Cape Town, South Africa
KanadeviaInova · Zürich, ZH, ch
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tsys · ATLANTA, GEORGIA
Design and build a new cloud-based data platform that consolidates SaaS data into Snowflake/Databricks/Fabric, including ETL pipelines and analytics-ready warehouses.
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 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
Everest Technologies