לוח משרות דרושים ראש צוות דאטה בתל אביב יפו

We are looking for a Data Engineering Team Lead to drive the development and optimization of our data infrastructure.

So, what will you be doing all day?

Lead & Mentor: Provide technical leadership and mentorship, setting clear goals, fostering innovation, managing stakeholders and guiding the professional growth of the team.
Data Infrastructure Development: Design and enhance scalable data architectures and pipelines to support current and future business needs.
ETL & Data Processing: Develop and maintain ETL processes, integrating data from various sources (APIs, databases, external platforms) using Python, SQL, and cloud technologies.
Cloud & Big Data Technologies: Implement solutions using PySpark, Databricks, Airflow, and cloud platforms (AWS) to process large-scale datasets efficiently.
Monitoring & Optimization: Ensure system reliability, observability, and performance using monitoring, logging, and alerting tools.
Collaboration & Stakeholder Engagement: Work closely with BI analysts, business stakeholders, and cross-functional teams to define data requirements and enable self-service BI capabilities.
Data Modeling: Design and maintain logical and physical data models to support analytics and operational needs.

We're looking for a Big Data DBA Team Lead to join our Data Operations (Data Ops) team in Tel Aviv.
The Data Operations Team's primary responsibility is to build, support and maintain the data pipelines infrastructure and efficient data stores.
We work on improving reliability, scalability and efficiency of data pipelines and data stores by engineering solutions that hosts the large amount of data we use as well as monitor, alert and auto-remediate issues that rises.
What Youll Do:
You will play a critical role in leading a team that ensures the stability, performance, and development of our organization's databases.
Your key responsibilities will include leading a DBA team which runs daily database management, performance tuning, database design and architecture, backup and recovery responsibilities.
You will work closely with R&D, BI teams and project managers on new and ongoing data-related projects.
Manage the team roadmap, team building and project execution.
Collaborate with stakeholders including data engineers, data analyst, BI developers and product managers.
Take full ownership of end-to-end data infrastructure, from design to production. Contribute to building and designing innovative big data architecture and pipelines.

Required Data Team Lead
About the Team
The ML DataOps team is building cutting-edge data infrastructure to enable machine learning teams to utilize high-quality, real-time data at scale. Our team plays a critical role in enabling ML innovation by developing and maintaining a robust feature store, one of the top-selling products within our group.
We specialize in building scalable and reliable feature platforms that support:
Batch processing for large-scale analytics
Streaming pipelines for real-time feature availability
Streaming aggregation, a particularly challenging engineering problem that we have successfully tackled to ensure low-latency, high-accuracy and efficient ML workflows
Our work requires deep expertise in distributed systems, data engineering, and real-time processing, as well as the ability to deliver at high velocity while maintaining high quality. We are continuously improving our infrastructure to support the most demanding ML workloads.
As a Team Lead you will...
Lead and mentor a growing team of skilled data oriented engineers.
Own the execution and delivery of the teams roadmap, ensuring high-quality and timely releases.
Work closely with Product, ML, and other engineering teams to align priorities and technical requirements.
Build the infrastructure that enables customers to optimize large-scale batch & streaming data pipelines for ML workloads.
Develop solutions for real-time data processing, analytics, and data consistency.
Maintain and evolve our Feature store, ensuring reliability and scalability.
Define and enforce coding standards, methodologies, and technical direction for the team.
Proactively identify opportunities for improvement in workflows, architecture, and team processes.