Experienced Data Engineer
Indexed description
You've worked with cloud platforms, modern data stacks, distributed systems, and large datasets. You understand that good engineering is about building solutions that remain reliable, scalable, and maintainable.
You're curious and you enjoy understanding data flows, dependencies, bottlenecks, and architecture decisions. You like connecting the dots between business problems, data, infrastructure, and software engineering.
You probably have several years of experience as a Data Engineer, or comparable positions, ideally working with large-scale environments, and you're comfortable taking ownership of technical decisions. You're also someone who enjoys sharing knowledge and helping less experienced engineers grow.
Your Role And Responsibilities
As a Data Engineer in IBM Consulting, you'll work in environments where the requirements may change and every client has a different technical landscape. You'll be expected to navigate complexity and make pragmatic architectural decisions.
Depending On The Project, This Could Involve
- Designing and implementing modern data platforms
- Building robust ETL/ELT pipelines and data products
- Working with cloud-native services and infrastructure
- Enabling analytics, AI, and machine learning use cases
- Improving data quality, observability, governance, and reliability
- Automating workflows and reducing operational overhead
Required Technical And Professional Expertise
- Strong foundation in Python and SQL
- Experience with at least one of these cloud platforms (Azure, GCP, AWS)
- Experience working with scalable data pipelines (ETL/ELT) on a data platform (e.g. Databricks, Redshift, Snowflake)
- Experience in agile processes and methods
- Fluency in Swedish and English in speech and writing
- The right to work in Sweden without restrictions at the time of application
- Experience working with Spark (e.g., through PySpark)
- Experience building BI dashboards (e.g., PowerBI, Looker Studio)
- Good understanding of using and developing APIs (e.g., REST, GraphQL)
- Experience with real-time event-based data pipelines using Kafka and/or stream processing frameworks (e.g., Flink)
- Experience with CI/CD and infrastructure as code (IaC) (e.g., Terraform)
- Experience using and working with LLMs and VLMs
- Experience with vector databases and using them for a RAG solution
- Experience working with MLOps infrastructure (e.g., MLflow)
- Experience with other programming languages (e.g., Go, TypeScript, Java)
- Experience using common ML frameworks (e.g., PyTorch)
- Good communication and presentation skills, and the ability to present results from a notebook (e.g., Jupyter Notebook)
- Good understanding of data and AI governance (e.g., GDPR, European AI Act)
- Good understanding of Agentic AI
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