The company is looking to solve the problem of inefficient data flow and limited business insight delivery by developing scalable data pipelines and infrastructure.
Requirements
- Designed and implemented optimized ETL/ELT pipelines, reducing processing time and improving system reliability
- Applied advanced data modeling and transformation techniques, resulting in scalable, maintainable data architecture
- Developed robust data transformation workflows, including metadata management, dependency tracking, and workload orchestration
- Built and maintained pipelines using orchestration tools such as Apache Airflow or AWS Glue for seamless data ingestion and delivery
- Deep hands-on experience with relational databases (e.g., SQL Server) and NoSQL solutions (e.g., MongoDB), optimizing performance and reliability
- Expertise in database tuning and optimization, improving data access speed and reducing resource usage
- Proficient in cloud-based infrastructure, particularly AWS services like EC2, S3, Glue, and EMR (Spark), for scalable data solutions
Responsibilities
- Design, build, and maintain robust, secure, and scalable data pipelines and systems
- Assemble and curate large datasets to meet functional and non-functional business requirements
- Perform advanced data modeling, architecture design, and performance tuning
- Develop infrastructure to support real-time and batch processing workflows
- Optimize database performance and write advanced SQL queries for reporting and applications
- Resolve system and data issues with thorough root cause analysis and long-term fixes
- Lead internal process improvements and scalability efforts across the data stack
Other
- Proven track record of delivering scalable data solutions, backed by 4+ years of progressive experience as a Data Engineer and/or an Associate degree in Computer Science or equivalent practical experience
- Demonstrated ability to lead cross-functional projects and mentor junior engineers, contributing to stronger team performance and higher-quality output
- Effectively supported and collaborated with cross-functional teams including Product, Engineering, and Analytics to deliver business-aligned data solutions
- Mentored junior data engineers, enhancing team capability, reducing onboarding time, and driving knowledge sharing
