דרושים » דאטה » Senior Data Engineer

This role has been designed as Hybrid with an expectation that you will work on average 2 days per week from an office.

We are looking for a talented Data Engineer to help build and enhance the data platform that supports analytics, operations, and data-driven decision-making across the organization. You will work hands-on to develop scalable data pipelines, improve data models, ensure data quality, and contribute to the continuous evolution of our modern data ecosystem.

Youll collaborate closely with Senior Engineers, Analysts, Data Scientists, and stakeholders across the business to deliver reliable, well-structured, and well-governed data solutions.


What Youll Do:

Engineering & Delivery

Build, maintain, and optimize data pipelines for batch and streaming workloads.

Develop reliable data models and transformations to support analytics, reporting, and operational use cases.

Integrate new data sources, APIs, and event streams into the platform.

Implement data quality checks, testing, documentation, and monitoring.

Write clean, performant SQL and Python code.

Contribute to improving performance, scalability, and cost-efficiency across the data platform.

Collaboration & Teamwork

Work closely with senior engineers to implement architectural patterns and best practices.

Collaborate with analysts and data scientists to translate requirements into technical solutions.

Participate in code reviews, design discussions, and continuous improvement initiatives.

Help maintain clear documentation of data flows, models, and processes.

Platform & Process

Support the adoption and roll-out of new data tools, standards, and workflows.

Contribute to DataOps processes such as CI/CD, testing, and automation.

Assist in monitoring pipeline health and resolving data-related issues.

We are looking for an experienced and hands-on Data Engineer to lead the migration of enterprise data platforms to Google Cloud Platform (GCP).
In this role, you will design, build and maintain scalable ETL/ELT pipelines, develop advanced data models in BigQuery and contribute to the creation of a high-performance, reliable and cost-efficient data architecture.
You will work closely with analysts, data scientists and engineers and have real impact on how data is consumed across the organization.
What You Will Do:
Lead the migration of data from on-premise core systems to Google Cloud Platform (GCP).
Design and develop processed data layers (Silver and Gold) and data marts in BigQuery, including complex business logic.
Build, orchestrate and maintain data pipelines using Cloud Composer / Apache Airflow.
Develop robust data transformations, including cleansing, enrichment and data quality improvements.
Write efficient and optimized SQL queries in BigQuery with strong focus on performance and cost.
Create and maintain clear and up-to-date technical documentation for data architecture and processes.

This role has been designed as Hybrid with an expectation that you will work on average 2 days per week from our office.

We are looking for a highly skilled Senior Data Engineer with strong architectural expertise to design and evolve our next-generation data platform. You will define the technical vision, build scalable and reliable data systems, and guide the long-term architecture that powers analytics, operational decision-making, and data-driven products across the organization.

This role is both strategic and hands-on. You will evaluate modern data technologies, define engineering best practices, and lead the implementation of robust, high-performance data solutions-including the design, build, and lifecycle management of data pipelines that support batch, streaming, and near-real-time workloads.

What Youll Do

Architecture & Strategy

Own the architecture of our data platform, ensuring scalability, performance, reliability, and security.
Define standards and best practices for data modeling, transformation, orchestration, governance, and lifecycle management.
Evaluate and integrate modern data technologies and frameworks that align with our long-term platform strategy.
Collaborate with engineering and product leadership to shape the technical roadmap.

Engineering & Delivery

Design, build, and manage scalable, resilient data pipelines for batch, streaming, and event-driven workloads.
Develop clean, high-quality data models and schemas to support analytics, BI, operational systems, and ML workflows.
Implement data quality, lineage, observability, and automated testing frameworks.
Build ingestion patterns for APIs, event streams, files, and third-party data sources.
Optimize compute, storage, and transformation layers for performance and cost efficiency.

Leadership & Collaboration

Serve as a senior technical leader and mentor within the data engineering team.
Lead architecture reviews, design discussions, and cross-team engineering initiatives.
Work closely with analysts, data scientists, software engineers, and product owners to define and deliver data solutions.
Communicate architectural decisions and trade-offs to technical and non-technical stakeholders.

As part of our Data Engineering team, you will not only build scalable data platforms but also directly enable portfolio growth by supporting new funding capabilities, loan sales and securitization, and improving cost efficiency through automated and trusted data flows that evolve our accounting processes.

Responsibilities

Design and build data solutions that support Sunbits core business goals, from enabling capital market transactions (loan sales and securitization) to providing
reliable insights for reducing the cost of capital.
Develop advanced data pipelines and analytics to support finance, accounting, and product growth initiatives.
Create ELT processes and SQL queries to bring data to the data warehouse and other data sources.
Develop data-driven finance products that accelerate funding capabilities and automate accounting reconciliations.
Own and evolve data lake pipelines, maintenance, schema management, and improvements.
Create new features from scratch, enhance existing features, and optimize existing functionality.
Collaborate with stakeholders across Finance, Product, Backend Engineering, and Data Science to align technical work with business outcomes.
Implement new tools and modern development approaches that improve both scalability and business agility.
Ensure adherence to coding best practices and development of reusable code.
Constantly monitor the data platform and make recommendations to enhance architecture, performance, and cost efficiency.

We're seeking an outstanding and passionate Data Platform Engineer to join our growing R&D team.

You will work in an energetic startup environment following Agile concepts and methodologies. Joining the company at this unique and exciting stage in our growth journey creates an exceptional opportunity to take part in shaping Finaloop's data infrastructure at the forefront of Fintech and AI.

What you'll do:

Design, build, and maintain scalable data pipelines and ETL processes for our financial data platform.

Develop and optimize data infrastructure to support real-time analytics and reporting.

Implement data governance, security, and privacy controls to ensure data quality and compliance.

Create and maintain documentation for data platforms and processes.

Collaborate with data scientists and analysts to deliver actionable insights to our customers.

Troubleshoot and resolve data infrastructure issues efficiently.

Monitor system performance and implement optimizations.

Stay current with emerging technologies and implement innovative solutions.

We are seeking a Senior Backend Engineer - Data Platform to join our expanding team and play a crucial role in designing, building, and maintaining robust and scalable data pipelines and infrastructure. In this role, you will directly enable data-driven decision-making and support the development and deployment of AI/ML products that power Health.

Youll collaborate closely with engineering, product, and data science teams to ensure our data systems are high-quality, resilient, and scalable as we grow. As a Senior Backend Engineer on our Data Platform team, you will drive efforts to deliver reliable, efficient, and consistent data services across the organization. You will also help enable the rapid development and deployment of advanced features, insights, and AI-driven capabilities that improve outcomes for clinicians and clients.

Who are you?
You are a seasoned backend or data engineer with experience working on production-grade ML/AI-powered products. You thrive in fast-paced, high-ownership environments and are passionate about building scalable and reliable systems. You understand the unique requirements of delivering AI/ML features in production, and you are comfortable working with modern technologies in the LLM/RAG ecosystem.
You pride yourself on delivering high-quality solutions quickly, without sacrificing design or reliability. Youre known for your responsiveness, collaborative spirit, and service-oriented mindset-especially when youre on-call and the stakes are high.How will you contribute?
Design, implement, and maintain scalable and reliable data pipelines and backend systems supporting both operational and analytical needs, with a focus on ML/AI product enablement.
Ensure data processing is optimized for speed, efficiency, and fault tolerance, enabling seamless integration with AI/ML workflows and reliable performance across all our Health products.
Monitor and improve uptime, reliability, and observability of our data infrastructure and pipelines.
Build and maintain systems to ensure data quality, consistency, and usability across the organization, enabling advanced analytics and AI solutions.
Work closely with product and engineering teams to deliver new features rapidly and with a high standard of technical excellence.
Drive innovation in how we build, measure, and optimize data features, backend services, and AI product integrations.
Participate in on-call rotations with a service-oriented approach and fast responsiveness.
Lead scalability efforts to support increasing data volumes, expanding AI/ML initiatives, and new product launches.

Required ML Data Engineer
Israel: Tel Aviv/ Hybrid (Israel)
R&D | Full Time | Job Id: 24792
Key Responsibilities
Your Impact & Responsibilities:
As a Data Engineer - AI Technologies, you will be responsible for building and operating the data foundation that enables our LLM and ML research: from ingestion and augmentation, through labeling and quality control, to efficient data delivery for training and evaluation.
You will:
Own data pipelines for LLM training and evaluation
Design, build and maintain scalable pipelines to ingest, transform and serve large-scale text, log, code and semi-structured data from multiple products and internal systems.
Drive data augmentation and synthetic data generation
Implement and operate pipelines for data augmentation (e.g., prompt-based generation, paraphrasing, negative sampling, multi-positive pairs) in close collaboration with ML Research Engineers.
Build tagging, labeling and annotation workflows
Support human-in-the-loop labeling, active learning loops and semi-automated tagging. Work with domain experts to implement tools, schemas and processes for consistent, high-quality annotations.
Ensure data quality, observability and governance
Define and monitor data quality checks (coverage, drift, anomalies, duplicates, PII), manage dataset versions, and maintain clear documentation and lineage for training and evaluation datasets.
Optimize training data flows for efficiency and cost
Design storage layouts and access patterns that reduce training time and cost (e.g., sharding, caching, streaming). Work with ML engineers to make sure the right data arrives at the right place, in the right format.
Build and maintain data infrastructure for LLM workloads
Work with cloud and platform teams to develop robust, production-grade infrastructure: data lakes / warehouses, feature stores, vector stores, and high-throughput data services used by training jobs and offline evaluation.
Collaborate closely with ML Research Engineers and security experts
Translate modeling and security requirements into concrete data tasks: dataset design, splits, sampling strategies, and evaluation data construction for specific security use.

We are looking for a talented Data Engineer to join our analytics team in the Big Data Platform group.
Job Id: 25380
You will support our product and business data initiatives, expand our data warehouse, and optimize our data pipeline architecture with an AI first attitude.
The ideal candidate is experienced in leveraging AI tools as part of modern data pipeline development, enabling scalable solutions, accelerating delivery, and continuously exploring new approaches and technologies.
The right candidate is excited by the prospect of building the data architecture for the next generation of products and data initiatives.
This is a unique opportunity to join a team full of outstanding people making a big impact on us.
We work on multiple products in many domains to deliver truly innovative solutions in the Cyber Security and Big Data realm.
This role requires the ability to collaborate closely with both R&D teams and business stakeholders, to understand their needs and translate them into robust and scalable data solutions.
Key Responsibilities
Maintain and develop enterprise-grade Data Warehouse and Data Lake environments
Create data infrastructure for various R&D groups across the organization to support product development and optimization
Work with data experts to assist with technical data-related issues and support infrastructure needs
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, redesigning infrastructure for scalability
Build and maintain robust ETL/ELT pipelines for data ingestion, transformation, and delivery across various systems
Incorporate AI-assisted tools into data pipeline design, development, and optimization to improve efficiency, scalability, and innovation

looking for a Data Engineer to help build and scale our analytics data infrastructure. In this role, you will work closely with analysts and business stakeholders to design reliable data models and support the development of a centralized semantic layer used across the company.

You will play a key role in improving the structure, reliability, and usability of our data stack. This includes building and maintaining dbt models, supporting data pipelines, and ensuring analysts have access to clean, well-documented, and consistent data.

This role is ideal for someone who enjoys working at the intersection of data engineering and analytics - translating business needs into scalable data models and enabling teams to move faster with trusted data.

Responsibilities

Design and implement data models that support analytics across key business domains such as GTM, CX, and Finance
Build and maintain transformation workflows using dbt
Work closely with analysts to translate business questions into scalable and reusable data models
Help define and implement a structured semantic layer that enables consistent metrics across the company
Improve the reliability and clarity of the analytics data stack by centralizing logic into well-designed data models
Support the ingestion and transformation of data from various sources using tools such as Fivetran and Airbyte
Contribute to improving data quality, monitoring, and documentation practices
Help establish best practices for analytics modeling and data usage across teams
Actively leverage AI tools (e.g. Cursor, LLM-based assistants) to improve development speed, data modeling, and data workflows

We are looking for an experienced Data Engineering Team Leader.
In this role, you will lead and strengthen our Data Team, drive innovation, and ensure the robustness of our data and analytics platforms.
A day in the life and how youll make an impact:
Drive the technical strategy and roadmap for the data engineering function, ensuring alignment with overall business objectives.
Own the design, development, and evolution of scalable, high-performance data pipelines to enable diverse and growing business needs.
Establish and enforce a strong data governance framework, including comprehensive data quality standards, monitoring, and security protocols, taking full accountability for data integrity and reliability.
Lead the continuous enhancement and optimization of the data analytics platform and infrastructure, focusing on performance, scalability, and cost efficiency.
Champion the complete data lifecycle, from robust infrastructure and data ingestion to detailed analysis and automated reporting, to maximize the strategic value of data and drive business growth.