Data Team Lead

Cust2Mate is a global leader in smart-cart platforms, transforming in-store shopping through digitalization and personalization. Our award-winning Smart Carts, trusted by leading grocery chains, elevate the customer experience, optimize store operations, and bridge online and physical retail.

• 6+ years of hands-on experience in Data Engineering, Database Engineering, Data Architecture, or a related field.

• Experience leading, mentoring, or providing technical leadership to data engineers.

• Strong hands-on RDBMS experience, including data modeling, schema design, indexing, query optimization, performance troubleshooting, and production database design.

• Experience with NoSQL databases and a strong understanding of the architectural trade-offs between relational, document, analytical, and other data storage approaches.

• Strong experience designing data warehouses, analytical data models, and modern cloud-based data platforms.

• Strong experience designing ETL/ELT, data ingestion, and data processing architectures.

• Strong SQL skills and practical experience with a programming language commonly used for data engineering and automation.

• Experience designing data platforms with scalability, reliability, maintainability, security, privacy, and cost considerations as part of the architecture.

• Good understanding of data quality, governance, lineage, lifecycle management, and batch and event-driven processing patterns.

• Ability to combine architecture-level ownership with hands-on investigation and implementation, while effectively guiding less-experienced engineers.

Advantages

• Strong PostgreSQL experience.

• Experience with MongoDB and MongoDB Atlas.

• Hands-on experience with Google Cloud Platform, particularly BigQuery and Dataplex.

• Experience with Airflow or similar workflow orchestration platforms.

• Strong Python experience for data engineering and automation.

• Experience designing systems for high-volume telemetry, IoT, or event data.

• Experience with multi-tenant and distributed data architectures.

• Experience designing and implementing data flows from operational databases to analytical platforms, selecting appropriate patterns such as batch processing, CDC, replication, and event-driven architectures based on performance, latency, scalability, cost, reliability, and security.

• Experience with database schema evolution and data migrations, including safely restructuring and transforming data as application data models evolve.

• Experience with data lakes, data catalogs, data quality frameworks, or data lineage solutions.

• Experience supporting analytical, BI, machine-learning, or data-science use cases.