Descripción de la oferta
## Data EngineerMalaga,Andalusia,SpainFind out how well you match with this jobJob ID786543**Our Exciting Opportunity**As a leader in mobile communications and network innovation, Ericsson offers the opportunity to shape the future of connectivity through data, software, and cloud technologies. In this role, you will contribute to high-impact initiatives in CNS CSE, working with experienced engineering teams to build production-grade data solutions that support product development, operational insight, and continuous improvement. Ericsson values expertise, collaboration, and curiosity, and provides an environment where you can grow while making a measurable impact.Within Solution Area Cognitive Network Solutions (SA CNS), CNS CSE offers the opportunity to work at the intersection of product engineering, data, and cloud-native technology. As a Senior Data Engineer in the Architecture & Data Management team, you will design and evolve robust data pipelines, reusable data capabilities, and governed data services that support product teams, research and development activities, and internal stakeholders. The role combines hands-on engineering with strong ownership of data quality, secure data handling, metadata, lineage, and operational excellence across the data lifecycle, with a particular focus on AWS-based data environments, S3-backed data flows, and data enablement for analytics and product use cases.**What will you bring:*** A degree in Computer Science, Software Engineering, Data Engineering, or a related technical field.* 5+ years of experience in data engineering or a closely related software engineering role, with proven expertise in designing and delivering production-grade data solutions.* Strong experience in data modeling, scalable batch data pipelines, and data lake or data warehouse patterns, with the ability to support near real-time use cases where required.* Strong SQL skills and solid experience with relational databases, including schema design, performance optimization, and data access patterns.* Strong programming skills in Python, with experience building and maintaining robust data processing code and reusable pipeline components.* Hands-on experience with workflow orchestration tools such as Prefect, or equivalent orchestration frameworks used for production data processing.* Strong hands-on experience building and operating event-driven data pipelines across AWS services such as Lambda, ECS Fargate, Batch, Step Functions, Glue, Athena, EventBridge, and S3, using Python and Bash in cloud-native data processing workflows.* Experience exposing data for analytics, reporting, or catalog use cases, with familiarity in Power BI and similar tools considered an advantage.* Practical experience with data catalog, metadata, lineage, governance, and secure data handling practices in enterprise environments.* Understanding of access control, consent-aware data usage, and secure data management in environments handling customer or operator data.* Strong understanding of DevOps practices, Git-based development workflows, automated testing, and CI/CD pipelines for reliable software and data delivery.* Experience using AI-assisted engineering tools responsibly to support coding, documentation, analysis, and troubleshooting in line with security and governance requirements.* Experience with containerization and platform technologies such as Docker and Kubernetes, as well as monitoring, observability, and operational support in production environments.* Ability to collaborate effectively with architects, product teams, R&D stakeholders, and data consumers to translate business or product needs into scalable data solutions.* Experience working with raw and parsed datasets, dataset onboarding, migration, or data enablement workflows is considered a strong advantage.* Experience in telecom, network analytics, product engineering, or similarly complex engineering domains is considered a strong advantage.**What will you do?*** Design, build, and optimize robust end-to-end data pipelines and reusable processing components that deliver trusted and timely data to product teams and other stakeholders.* Develop, maintain, and operate AWS-based, event-driven data workflows and S3-backed data flows, ensuring secure, scalable, and reliable data movement across the delivery lifecycle.* Build and evolve orchestrated data processing workflows using tools such as Prefect, with strong focus on quality, maintainability, and operational support.* Collaborate with cross-functional engineering teams, architects, and stakeholders to design scalable data solutions aligned with product, analytics, and operational needs.* Drive implementation of data ingestion, transformation, validation, and quality assurance frameworks, including support for raw and parsed dataset workflows.* Contribute to the evolution of data catalog, metadata, and lineage capabilities to improve discoverability, governance, and controlled data consumption.* Support secure data handling, access control, and consent-aware ways of working in collaboration with relevant stakeholders and governance processes.* Enable data consumption for analytics and reporting scenarios, including integration points that support cataloging, dashboards, and internal visibility of available datasets.* Independently drive improvements to data platform capabilities, pipeline performance, testing, observability, and operational excellence.* Use modern engineering practices and AI-assisted tools where appropriate to improve development efficiency, documentation quality, troubleshooting, and delivery excellence while following security and governance requirements.* Take ownership of engineering standards, reusable patterns, documentation, and best practices for data engineering across the team.* Communicate technical solutions, trade-offs, and delivery progress effectively to engineering teams, product stakeholders, and leadership in both technical and plain language.* Mentor peers when needed and contribute to a strong engineering culture focused on quality, collaboration, continuous learning, and pragmatic adoption of better ways of working.
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