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.

We are looking for a hands-on Data Team Lead to lead and develop our data engineering team and take end-to-end ownership of the company’s data platforms.

Responsibilities:

• Lead, mentor, and develop a small team of data engineers, providing technical guidance, design reviews, and hands-on support.
• Own the architecture and technical direction of the company’s data platforms, covering application databases, data warehouse, and analytical systems.
• Provide database architecture and DBA expertise, including data modeling, schema design, indexing, performance, capacity planning, scalability, and database technology selection.
• Design data ingestion, ETL/ELT, and processing architectures, including appropriate use of batch and event-driven patterns, with the ability to evolve from the current scale to millions or billions of telemetry events.
• Take technical ownership of data analysis, aggregation, transformation, and enrichment processes used by Product, R&D, Operations, and business stakeholders.
• Ensure data platform designs address security and privacy requirements, including data isolation, access patterns, sensitive data handling, retention, and appropriate protection of data throughout its lifecycle.
• Establish engineering standards and best practices for data quality, governance, lineage, observability, testing, documentation, and maintainability.
• Work closely with application and platform teams on data-related architecture and implementation, while continuously evaluating performance, scalability, reliability, and cost trade-offs.

Requirements:

• 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.