Data Team Lead

Description

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 talented and motivated individuals to join us and be a part of this fascinating journey.

Role Overview

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. The role combines technical leadership, Application DBA expertise, and data architecture, with hands-on involvement in database design, performance optimization, troubleshooting, and architectural decisions. You will work closely with software development teams on production applications and databases, while owning the architecture and evolution of both our application data platforms and analytical data warehouse. You will also lead data ingestion, transformation, aggregation, and enrichment processes, ensuring our data platforms are scalable, reliable, secure, and cost-effective.

Key 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.
• Serve as the technical owner of the application database layer, working closely with software development teams on database architecture, schema design, performance optimization, troubleshooting, migrations, and production issues.
• Provide hands-on Application DBA expertise, including data modeling, schema design, indexing, query optimization, performance troubleshooting, capacity planning, scalability, and database technology selection.
• Work closely with software development teams throughout the development lifecycle, ensuring database decisions support application performance, reliability, scalability, and maintainability.
• Work hands-on with both relational and non-relational databases, understanding the architectural trade-offs between different storage technologies and selecting the appropriate solution for each use case.
• Lead the design and evolution of the company’s analytical data platform and data warehouse, including analytical data models, data ingestion, transformation, and processing.
• Design and implement data flows between operational/application databases and analytical platforms, using appropriate patterns such as batch processing, CDC, replication, and event-driven architectures.
• Design data ingestion, ETL/ELT, and processing architectures, with the ability to evolve from the current scale to millions or billions of telemetry and event records.
• 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.
• Own and guide database schema evolution and migrations, ensuring application data models can evolve safely without compromising data integrity or production availability.
• Work closely with application and platform teams on data-related architecture and implementation, continuously evaluating performance, scalability, reliability, security, and cost trade-offs.

Requirements

• 6+ years of hands-on experience in Data and Database Engineering, Application DBA.
• Experience working closely and directly with software development / R&D teams on database architecture, implementation, troubleshooting, and optimization.
• Strong hands-on experience with relational databases, including schema design, query optimization, performance troubleshooting, migrations, and production database design.
• Experience with NoSQL databases, with a strong understanding of the differences and architectural trade-offs between relational, document, analytical, and other data storage approaches.
• Strong experience designing and building data warehouses and analytical data platforms, including analytical data models and modern cloud-based data environments.
• Strong experience designing ETL/ELT, data ingestion, including batch processing, CDC, replication, and event-driven architectures.
• Good understanding of data quality, governance, lineage, lifecycle management, and batch and event-driven processing patterns.
• 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 supporting analytical, BI, machine-learning, or data-science use cases.