דרושים » ניהול ביניים » Senior data engineer עבור חברת Big Data בתל אביב

As our Senior Data Engineer you will handle Big Data in real time, and become the owner and gatekeeper of all data and data flows within the company. You will bring data ingenuity and technological excellence while gaining a deep business understanding.

This is an amazing opportunity to join a multi-disciplinary A-team while working in a fast-paced, modern-cloud, data-oriented environment.

Implement robust, reliable, and scalable data pipelines and data architecture.
Own and develop DWH (petabytes of data!).
Own the entire data development process, including business knowledge, methodology, quality assurance, and monitoring.
Collaborate with cross-functional teams to define, design, and ship new features.
Continuously discover, evaluate, and implement new technologies to maximize development efficiency.
Develop tailor-made solutions as part of our data pipelines.
Lead complex Big Data projects and build data platforms from scratch.
Work on high-scale, real-time, real-world, business-critical data stores and data endpoints.
Implement data profiling to identify anomalies and maintain data integrity.
Work in a results-driven, high-paced, rewarding environment.

we are looking for a Data Infrastructure Lead.
As the Data Infrastructure Lead, you will own the technical direction, architecture, and delivery of Skais core production data infrastructure (MySQL, SingleStore, Snowflake, Airflow, and Microservices).
In this role, you will balance in-house, open-source, and SaaS solutions to manage petabyte-scale data while ensuring system reliability, scalability, and cost-efficiency. You will blend hands-on engineering, technical architecture, vendor management, and team mentorship. You will lead cross-functional delivery, drive operational excellence across a 24/7 production environment, and set engineering guardrails for cloud-based data systems across the company.
Responsibilities:
Technical Leadership & Architecture: Set engineering guardrails, lead architectural reviews, lead the pods daily, and balance design tradeoffs for cloud-based data systems across all business units.
Execution & Delivery: Own the pods Scrum processes (epics, stories, tracking), balance technical debt management with rapid feature delivery, and drive multi-project execution.
Hands-on Engineering & Escalations: Stay active in code and infrastructure, serving as the primary technical escalation point for complex, 24/7 production issues.
Ops, High Availability & Reliability: Oversee operational readiness, rotation schedules, high availability (HA), backup and recovery processes, and automation to maintain 24/7 production uptime.
Mentorship & Team Culture: Raise the technical bar, foster a culture of engineering excellence, mentor team members, and drive professional growth within the pod.
Stakeholder & Vendor Alignment: Act as a strategic partner to align roadmap planning, bridge technical execution with business goals, and serve as the main point of contact for data infrastructure vendors.

We're looking for an experienced Software Engineer to join our Data Platform Group. In this key role, you will build the company data platform: cloud-based microservices and data pipelines that process on the order of 1M records/sec at low latency. Because the platform is the foundation other groups build on, your work has a direct impact on our customers and enables engineering, product, and research teams across the organization.
The Lakehouse team owns the data itself - how it is stored, organized, retained, and served. We own our analytical storage layer, the batch processing built on top of it, and the APIs through which customers and the rest of the company consume data. If you enjoy the problems that only appear at petabyte scale - physical data layout, query performance, storage cost, and retention - this is the role.

Responsibilities:
End-to-end ownership of our large-scale analytical storage layer: data modeling, schema and table design, partitioning, retention, and query performance.
Design and develop the batch processing layer over our data lake using Spark on EMR (Java and PySpark).
Build and evolve Java/Spring Boot services that expose our data through well-defined APIs to customers and to consumers across the company.
Own performance and cost: query optimization, file layout and compaction, cluster sizing, and storage efficiency at scale.
Research new technologies in the lakehouse and analytical-storage space and adapt them for use in our product.
Work closely with product, DevOps, and security teams.