AI-Powered Data Platform Engineer

Ericsson; Stockholms kommun; On-site; SEK 900,000 - 1,300,000; Full time

Job summary

Ericsson invites you to join a greenfield initiative in the RAN organization, building an AI-powered data platform that keeps design intent aligned with implementation and enables analytics for product teams.

As a Systems Data Engineer, you will design end-to-end data pipelines, implement event-driven workflows with Prefect and cloud services, ensure data quality, governance, and secure data handling across the data lifecycle, from ingestion to analytics.

Qualifications

• Degree in Computer Science, Software Engineering, Data Engineering, or related technical field
• 5+ years of experience in data engineering or closely related software engineering role
• Strong experience in data modelling, scalable batch data pipelines, and data lake or data warehouse patterns
• Strong SQL skills and experience with relational databases, schema design and performance optimization
• Strong programming skills in Python for data processing and reusable pipeline components
• Hands‑on experience with workflow orchestration tools such as Prefect or equivalent
• Hands‑on experience building and operating event‑driven data pipelines across different platform services (Lambda, ECS Fargate, Batch, Step Functions, Glue, Athena, EventBridge, S3)
• Experience with Bash scripting in cloud‑native data processing workflows
• Practical experience with data catalogue, metadata, lineage, governance, and secure data handling practices

Responsibilities

• 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 platform-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 catalogue, 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 cataloguing, dashboards, and internal visibility of available datasets.
• 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.
• Communicate technical solutions, trade-offs, and delivery progress effectively to engineering teams, product stakeholders, and leadership in both technical and plain language.

Skills

Data modeling

Batch pipelines

SQL skills

Python

Prefect

Event-driven pipelines

Cloud services

Bash scripting

Data governance

Metadata & lineage

Education

Degree in Computer Science, Software Engineering, Data Engineering, or related technical field

Tools

Lambda

ECS Fargate

Batch

Step Functions

Glue

Athena

EventBridge

S3

CI/CD

Monitoring

Job description

Ericsson invites you to join a greenfield initiative in the RAN organization, building an AI-powered data platform that keeps design intent aligned with implementation and enables analytics for product teams.

As a Systems Data Engineer, you will design end-to-end data pipelines, implement event-driven workflows with Prefect and cloud services, ensure data quality, governance, and secure data handling across the data lifecycle, from ingestion to analytics.