Data Engineer at QAD, Inc.

Barcelona, Catalonia, Spain -

Full Time

Start Date

Immediate

Expiry Date

05 Feb, 26

Salary

0.0

Posted On

07 Nov, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, ETL, ELT, Python, Cloud Platforms, AWS, GCP, Azure, Data Integration, MLOps, Vector Databases, Performance Optimization, Collaboration, Data Quality, Data Processing, Monitoring

Industry

Software Development

Description

Company Description QAD is building a world-class SaaS company, and we are growing. We are looking for talented individuals who want to join us on our mission to help solve relevant real-world problems in manufacturing and the supply chain. We are a virtual first company and your primary work experience will be virtual / working from your home. Occasional travel to a physical office may be required to enhance working relationships, collaboration, design, strategy and alignment. Job Description About the Engineering Team The Engineering team, based in the US and Europe, is responsible for the design, development, and deployment of the organization's core products, with a focus on efficiency and speed. We architect and implement comprehensive solutions, including tools and platforms, to address key business requirements. These solutions encompass critical areas such as provisioning, configuration, continuous integration/continuous delivery (CI/CD), monitoring, service level agreements (SLAs), performance optimization, and system uptime. The team is committed to meticulous execution and collaborates extensively with a broad range of stakeholders throughout the product lifecycle. What Will You Do: As a Data Engineer, you will be the backbone for our advanced AI/ML initiatives, ensuring data is available, reliable, and scalable for model development and production. Your tasks will include: Gather the data coming from the ERP into Snowflake, design and build data pipelines: Architect, construct, and optimize robust ETL/ELT pipelines to ingest, process, and transform structured and unstructured data (including text, images, and multimodal datasets). Infrastructure for AI: Collaborate closely with Data Scientists to build and maintain the foundational data infrastructure and machine learning pipelines (MLOps) for deploying and serving AI/ML models in production environments. Data Integration: Integrate and manage data from various sources, ensuring data quality, reliability, and accessibility across the organization. Cloud Deployment: Utilize cloud platforms (AWS, GCP, or Azure) to provision, configure, and manage scalable data storage and processing services. Vector Database Management: Implement and maintain vector databases (e.g., FAISS, Pinecone) and data retrieval systems to support advanced Retrieval-Augmented Generation (RAG) architectures. System Optimization: Focus on performance optimization, monitoring, and system uptime for data services and integrated applications. Collaboration: Work with software engineers and data scientists to seamlessly integrate validated data and AI models into scalable, core applications. Qualifications Required Qualifications: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field. 1+ years of professional experience in a Data Engineering, Software Engineering, or MLOps-focused role. Strong programming skills in Python and experience with data processing frameworks (e.g., Spark, Pandas, Dask). Deep understanding of data engineering principles, ETL/ELT pipelines, and data modeling. Familiarity with data storage solutions, including SQL/NoSQL databases and cloud solutions, e.g. Snowflake. Experience with cloud platforms such as AWS, GCP, or Azure for data storage and processing deployments. Experience with CI/CD practices and deploying production systems. Preferred Qualifications: Experience managing data specifically for NLP/LLM applications, including text processing and embedding generation. Hands-on experience with MLOps tools, workflow orchestration (e.g., Airflow, Kubeflow), model monitoring, and performance tuning. Experience with vector databases (e.g., FAISS, Pinecone) and building retrieval-based data systems. Exposure to building data streams and real-time processing architectures. Soft Skills: Good collaboration skills at all levels with cross-functional teams (especially Data Science). Highly developed ownership and creative thinking when solving data challenges. Analytical thinking and the ability to solve complex data and system problems. Process orientation and ability to build effective, repeatable solutions. Time management and organizational skills. Fluent English language skills. Additional Information About QAD: QAD Inc. is a leading provider of adaptive, cloud-based enterprise software and services for global manufacturing companies. Global manufacturers face ever-increasing disruption caused by technology-driven innovation and changing consumer preferences. In order to survive and thrive, manufacturers must be able to innovate and change business models at unprecedented rates of speed. QAD calls these companies Adaptive Manufacturing Enterprises. QAD solutions help customers in the automotive, life sciences, packaging, consumer products, food and beverage, high tech and industrial manufacturing industries rapidly adapt to change and innovate for competitive advantage. QAD is committed to ensuring that every employee feels they work in an environment that values their contributions, respects their unique perspectives and provides opportunities for growth regardless of background. QAD’s DEI program is driving higher levels of diversity, equity and inclusion so that employees can bring their whole self to work. We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. #LI-Remote

Responsibilities

As a Data Engineer, you will ensure data is available, reliable, and scalable for AI/ML initiatives. You will architect, construct, and optimize robust ETL/ELT pipelines and collaborate with Data Scientists to build foundational data infrastructure.