- Maintain and enhance existing data processes with a focus on reliability, scalability, and performance optimization
- Analyze and deeply understand existing data models, workflows, tables, and processing jobs
- Identify technical gaps, bottlenecks, inefficiencies, and opportunities for architectural improvements
- Design, optimize, and support large-scale data pipelines and transformation processes
- Drive modernization initiatives across the data platform and contribute to long-term architectural evolution
- Optimize complex SQL queries and improve data processing latency and efficiency
- Collaborate with product engineering and cross-functional teams on technical solutions and implementation strategies
- Contribute to both short-term delivery goals and long-term platform development initiatives
- Ensure adherence to best practices in data engineering, system reliability, and performance optimization
- Participate in technical discussions, solution design, and continuous improvement initiatives
- 5+ years of commercial experience in Data Engineering
- Expert-level Python skills
- Expert-level SQL skills with practical experience optimizing complex queries for performance and scalability
- Strong hands-on experience with DBT for data transformation and modeling
- Solid understanding of database concepts, including DDL, DML, indexing, normalization, and advanced SQL techniques
- Hands-on experience working with Snowflake
- Experience with Elastic Stack technologies
- Proven experience designing, building, and maintaining large-scale data pipelines
- Strong analytical thinking and problem-solving skills
- Ability to work independently in a high-autonomy engineering environment
- Experience driving technical initiatives from idea through implementation
- Strong communication and collaboration skills
- Upper-Intermediate English level or higher
WILL BE A PLUS
- Experience with Airflow for workflow orchestration