דרושים » תוכנה » Data Engineer

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 our 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 looking for a Data Engineer.
What youll do:
Design, build, and optimize large-scale data pipelines and workflows for both batch and real-time processing.
Architect and maintain Airflow-based orchestration frameworks to manage complex data dependencies and data movement.
Develop high-quality, maintainable data transformation and integration processes across diverse data sources and domains.
Lead the design and implementation of scalable, cloud-based data infrastructure ensuring reliability, performance, and cost efficiency.
Drive data modeling and data architecture practices to ensure consistency, reusability, and quality across systems.
Collaborate closely with Product, R&D, BizDev, and Data Science teams to define data requirements, integrations, and delivery models.
Own the technical roadmap for key data initiatives, from design to production deployment.

DRS RADA is a leading defense high-tech company specializing in radar systems development. We are seeking an experienced Data Engineer to join our data engineering team. In this role, you will play a crucial part in designing, developing, and maintaining scalable data pipelines and infrastructure to support our AI department. This is an opportunity to work with cutting-edge technologies in a fast-paced production environment, driving impactful, data-driven solutions for the business. Key Responsibilities:
* Design, develop, and optimize ETL/ELT pipelines for large-scale data processing.
* Work with a modern data stack, including Databricks (Spark, SQL), Apache Airflow, and Azure services.
* Troubleshoot and optimize queries and jobs for performance improvements.
* Implement best practices for data governance, security, and monitoring.
* Stay updated with industry trends and emerging technologies in data engineering. If you're passionate about building scalable data solutions and thrive in a fast-paced environment, we’d love to hear from you!

We use cutting-edge innovations in financial technology to bring leading data and features that allow individuals to be qualified instantly, making purchases at the point-of-sale fast, fair and easy for consumers from all walks of life.
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 our companys 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 are looking for a DevOps Engineer (Data Platform Group).
Main responsibilities:
Data Architecture Direction: Provide strategic direction for our data architecture, selecting the appropriate componments for various tasks. Collaborate on requirements and make final decisions on system design and implementation.
Project Management: Manage end-to-end execution of high-performance, large-scale data-driven projects, including design, implementation, and ongoing maintenance.
Cost Optimization: Monitor and optimize cloud costs associated with data infrastructure and processes.
Efficiency and Reliability: Design and build monitoring tools to ensure the efficiency, reliability, and performance of data processes and systems.
DevOps Integration: Implement and manage DevOps practices to streamline development and operations, focusing on infrastructure automation, continuous integration/continuous deployment (CI/CD) pipelines, containerization, orchestration, and infrastructure as code. Ensure scalable, reliable, and efficient deployment processes.
Our stack: Azure, GCP, Kubernetes, ArgoCD, Jenkins, Databricks, Snowflake, Airflow, RDBMS, Spark, Kafka, Micro-Services, bash, Python, SQL.

we are looking for a Data Engineer
We work in a flexible, hybrid model, so you can choose the home-office balance that works best for you.
Responsibilities
Design, build, and maintain scalable ETL/ELT pipelines to integrate data from diverse sources, optimizing for performance and cost efficiency.
Leverage Databricks and other modern data platforms to manage, transform, and process data
Collaborate with software teams to understand data needs and ensure data solutions meet business requirements.
Optimize data processing workflows for performance and scalability.

Were looking for a highly skilled and motivated Data Engineer to join the Resolve (formerly DevOcean) team .
In this role, youll be responsible for designing, building, and optimizing the data infrastructure that powers our SaaS platform.
Youll play a key role in shaping a cost-efficient and scalable data architecture while building robust data pipelines that serve analytics, search, and reporting needs across the organization.
Youll work closely with our backend, product, and analytics teams to ensure our data layer remains fast, reliable, and future-proof. This is an opportunity to influence the evolution of our data strategy and help scale a cybersecurity platform that processes millions of findings across complex customer environments
Roles and Responsibilities:
Design, implement, and maintain data pipelines to support ingestion, transformation, and analytics workloads.
Collaborate with engineers to optimize MongoDB data models and identify opportunities for offloading workloads to analytical stores (ClickHouse, DuckDB, etc.).
Build scalable ETL/ELT workflows to consolidate and enrich data from multiple sources.
Develop data services and APIs that enable efficient querying and aggregation across large multi-tenant datasets.
Partner with backend and product teams to define data retention, indexing, and partitioning strategies to reduce cost and improve performance.
Ensure data quality, consistency, and observability through validation, monitoring, and automated testing.
Contribute to architectural discussions and help define the long-term data platform vision.

We are seeking a Senior Backend & Data Engineer to join its SaaS Data Platform team.
This role offers a unique opportunity to design and build large-scale, high-performance data platforms and backend services that power our cloud-based products.
You will own features end to end-from architecture and design through development and production deployment-while working closely with Data Science, Machine Learning, DevOps, and Product teams.
Key Responsibilities:
Design, develop, and maintain scalable, secure backend services and data platforms on AWS
Build and operate batch and streaming ETL/ELT pipelines using Spark, Glue, Athena, Iceberg, Lambda, and EKS
Develop backend components and data processing workflows in a cloud-native environment
Optimize performance, reliability, and observability of data pipelines and backend services
Collaborate with ML, backend, DevOps, and product teams to deliver data-driven solutions
Lead best practices in code quality, architecture, and technical excellence
Ensure security, compliance, and auditability using AWS best practices (IAM, encryption, auditing).

The ideal candidate is not afraid of data in any form or scale, and is experienced with cloud services to ingest, stream, store, and manipulate data. The Data Engineer will support new system designs and migrate existing ones, working closely with solutions architects, project managers, and data scientists. The candidate must be self-directed, a fast learner, and comfortable supporting the data needs of multiple teams, systems, and products. The right candidate will be excited by the prospect of optimizing or re-designing our customers data architecture to support their next generation of products, data initiatives, and machine learning systems.





Summary of Key Responsibilities



To meet compliance and regulatory requirements, keep our customers data separated and secure.
Design, Build, and operate the infrastructure required for optimal data extraction, transformation, and loading from a wide variety of data sources using SQL, cloud migration tools, and big data technologies.
Optimize various RDBMS engines in the cloud and solve customers' security, performance, and operational problems.
Design, build, and operate large, complex data lakes that meet functional / non-functional business requirements.
Optimize various data types' ingestion, storage, processing, and retrieval, from near real-time events and IoT to unstructured data such as images, audio, video, documents, and in between.
Work with customers' and internal stakeholders including the Executive, Product, Data, Software Development and Design teams to assist with data-related technical issues and support their data infrastructure and business needs.

We are looking for an experienced and visionary Data Platform Engineer to help design, build and scale our BI platform from the ground up.
In this role, you will be responsible for building the foundations of our data analytics platform - enabling scalable data pipelines and robust data modeling to support real-time and batch analytics, ML models and business insights that serve both business intelligence and product needs.
You will be part of the R&D team, collaborating closely with engineers, analysts, and product managers to deliver a modern data architecture that supports internal dashboards and future-facing operational analytics.
If you enjoy architecting from scratch, turning raw data into powerful insights, and owning the full data lifecycle - this role is for you!
Responsibilities
Take full ownership of the design and implementation of a scalable and efficient BI data infrastructure, ensuring high performance, reliability and security.
Lead the design and architecture of the data platform - from integration to transformation, modeling, storage, and access.
Build and maintain ETL/ELT pipelines, batch and real-time, to support analytics, reporting, and product integrations.
Establish and enforce best practices for data quality, lineage, observability, and governance to ensure accuracy and consistency.
Integrate modern tools and frameworks such as Airflow, dbt, Databricks, Power BI, and streaming platforms.
Collaborate cross-functionally with product, engineering, and analytics teams to translate business needs into data infrastructure.
Promote a data-driven culture - be an advocate for data-driven decision-making across the company by empowering stakeholders with reliable and self-service data access.