Data Team Lead

Givatayim, Israel · Full-time · Intermediate

About The Position

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

About the Role
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.
This role combines technical leadership, data architecture, and strong database expertise. You will lead a small team of data engineers, providing technical guidance and mentoring while remaining actively involved in design, implementation, troubleshooting, and architectural decisions.
You will be responsible for the architecture and evolution of both our application data platforms and analytical data warehouse, ensuring they are scalable, maintainable, cost-effective, and designed with security and data privacy in mind.
The role also includes ownership of data analysis and enrichment processes, helping transform operational and telemetry data into reliable and useful datasets for product, business, and analytical use cases.

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.
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.
Hands-on experience with Google Cloud Platform, particularly BigQuery and Dataplex.
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.
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 existing data as application data models evolve between versions.
Experience with data lakes, data catalogs, data quality frameworks, or data lineage solutions.
Experience supporting analytical, BI, machine-learning, or data-science use cases.