Data Architect

Description

LSports is a leading global provider of sports data, dedicated to revolutionizing the industry through innovative solutions. We excel in sports data collection and analysis, advanced data management, and cutting-edge services like AI-based sports tips and high-quality sports visualization. As the sports data industry continues to grow, LSports remains at the forefront, delivering real-time solutions.

If you share our love of sports and tech, you've got the passion and will to better the sports-tech and data industries - join the team!

Responsibilities:

Build the foundations of LSports’ modern data architecture, supporting real-time, high-scale (Big Data) sports data pipelines and ML/AI use cases, including Generative AI.
Map the company’s data needs and lead the selection and implementation of key technologies across the stack: data lakes (e.g., Iceberg), databases, ETL/ELT tools, orchestrators, data quality and observability frameworks, and statistical/ML tools.
Design and build a cloud-native, cost-efficient, and scalable data infrastructure from scratch, capable of supporting rapid growth, high concurrency, and low-latency SLAs (e.g., 1-second delivery).
Lead design reviews and provide architectural guidance for all data solutions, including data engineering, analytics, and ML/data science workflows.
Set high standards for data quality, integrity, and observability. Design and implement processes and tools to monitor and proactively address issues like missing events, data delays, or integrity failures.
Collaborate cross-functionally with other architects, R&D, product, and innovation teams to ensure alignment between infrastructure, product goals, and real-world constraints.
Mentor engineers and promote best practices around data modeling, storage, streaming, and observability.
Stay up-to-date with industry trends, evaluate emerging data technologies, and lead POCs to assess new tools and frameworks — especially in the domains of Big Data architecture, ML infrastructure, and Generative AI platforms.

Requirements

At least 10 years of experience in a data engineering role, including 2+ years as a data architect with ownership over company-wide architecture decisions.
Proven experience designing and implementing large-scale, Big Data infrastructure from scratch in a cloud-native environment (GCP preferred).
Excellent proficiency in data modeling, including conceptual, logical, and physical modeling for both analytical and real-time use cases.
Strong hands-on experience with:
Data lake and/or warehouse technologies, with Apache Iceberg experience required (e.g., Iceberg, Delta Lake, BigQuery, ClickHouse)
ETL/ELT frameworks and orchestrators (e.g., Airflow, dbt, Dagster)
Real-time streaming technologies (e.g., Kafka, Pub/Sub)
Data observability and quality monitoring solutions
Excellent proficiency in SQL, and in either Python or JavaScript.
Experience designing efficient data extraction and ingestion processes from multiple sources and handling large-scale, high-volume datasets.
Demonstrated ability to build and maintain infrastructure optimized for performance, uptime, and cost, with awareness of AI/ML infrastructure requirements.
Experience working with ML pipelines and AI-enabled data workflows, including support for Generative AI initiatives (e.g., content generation, vector search, model training pipelines) — or strong motivation to learn and lead in this space.
Excellent communication skills in English, with the ability to clearly document and explain architectural decisions to technical and non-technical audiences.
Fast learner with strong multitasking abilities; capable of managing several cross-functional initiatives simultaneously.
Willingness to work on-site in Ashkelon once a week.