Data Engineer Full Time

The role
We are hiring a Data Engineer to design, build, and maintain scalable data pipelines and analytics infrastructure. You will work closely with product, engineering, and business teams to ensure reliable data collection, processing, storage, and reporting across multiple products and systems. This is a hands-on engineering role focused on data architecture, ETL pipelines, and analytics infrastructure.

What you’ll do
01 Design and build scalable data pipelines and ETL workflows
02 Integrate data from multiple sources and services
03 Develop and maintain data storage and processing systems
04 Ensure data quality, consistency, and reliability
05 Optimize data workflows for performance and scalability
06 Support reporting, analytics, and business intelligence initiatives
07 Collaborate with backend, product, and analytics teams
08 Monitor and troubleshoot data infrastructure issues
09 Document data models, pipelines, and architecture decisions

What we’re looking for
Must-have experience and skills for this role.

01 3+ years of experience in data engineering
02 Strong SQL and database design skills
03 Experience building ETL/ELT pipelines
04 Experience with Python or similar scripting languages
05 Understanding of data modeling and data warehousing concepts
06 Experience working with large datasets and distributed systems
07 Strong problem-solving and analytical skills

Nice to have

• Experience with cloud platforms (AWS, GCP, Azure)
• Familiarity with Kafka, streaming systems, or event-driven architectures
• Experience with PostgreSQL, MongoDB, Redis, or data warehouses
• Knowledge of analytics and BI platforms
• Experience with blockchain or Web3 data sources
• Exposure to machine learning data pipelines
• Tech stack
• Python
• SQL
• PostgreSQL
• MongoDB
• Redis
• Kafka
• AWS / GCP
• ETL / ELT

Why join

• Work on modern Web3, RWA, and digital products
• High ownership and visible product impact
• Close collaboration with engineering, product, and design
• Fast-paced environment with real production systems
• Remote-friendly across global time zones

What success looks like
01 Reliable and scalable data pipelines in production
02 High-quality and accessible data across teams
03 Efficient reporting and analytics infrastructure
04 Optimized data processing and storage systems
05 Strong collaboration between engineering and business teams

Hiring process
01 Application review
02 Technical screening (SQL and data engineering discussion)
03 Practical exercise or architecture review
04 Final discussion

How to apply
We review every relevant profile. Keep it concise and concrete.

• Resume or LinkedIn
• GitHub profile or project portfolio (preferred)
• Examples of data pipelines, platforms, or engineering projects