Do you want to be part of a technology company with an AI-first approach, shaping the invisible by blending AI innovation with human experience?
altermAInd is a company that offers solutions enabling organizations to overcome limits, uncover hidden opportunities, and drive transformative growth.
Location:
Flexible / Remote
About the Role
We are seeking a Data Engineer / Data Architect to lead data architecture and engineering initiatives. This role combines hands-on data engineering with strategic architecture planning, focusing on MongoDB-based solutions, performance optimization, and scalable data infrastructure design.
What will you do?
Lead data modeling initiatives in MongoDB, implementing MongoDB design patterns for both transactional and analytical workloads
Design ETL and CDC pipelines between MongoDB and external systems
Architect scalable data structures, evaluate data architecture alternatives, and propose migration strategies to different architectures when needed
Maintain databases through automated scripting, performance optimization, query performance evaluation, and index tuning
Implement data encryption and security measures to ensure compliance with governance standards
Who are you?
MongoDB expertise:
Advanced knowledge of MongoDB, MQL, aggregation framework, MongoDB design patterns, query optimization, and index management
Data modeling \& architecture:
Strong data modeling skills for both NoSQL and RDBMS environments, ability to evaluate different data architectures and propose migration strategies
Database knowledge:
Understanding of main data solutions and databases (relational, NoSQL, time-series, etc.) and their tradeoffs to effectively propose new solutions and migrations
Programming:
Proficient in Python for data processing and SQL for relational database operations
Security:
Experience implementing data encryption and security measures
Cloud \& DevOps:
Microsoft Azure data tools experience
What do we offer?
Competitive salary with performance-based bonus.
Remote-first culture and flexible working hours.
Annual budget for learning, certifications, and conferences in AI/ML and DevOps.
Modern equipment plus a stipend for your home office.
The chance to shape production GenAI systems from the ground up in a fast-growing company.