Hybrid Senior Data Engineer – Scalable Real-World Data Systems

at Yora Sigma

• Job title: Senior Data Engineer – Scalable Real-World Data Systems
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Airflow, Apache, AWS, Azure, BigQuery, Cloud, Docker, Google Cloud Platform, Kafka, Kubernetes, Python, Spark, SQL, Terraform
• Location requirements: Hybrid, Gothenburg, Sweden

About the role

• Senior Data Engineer designing and scaling data pipelines for high-volume ecosystems. Join Yora's mission to impact technology positively in autonomous systems and AI.

Responsibilities

• Architect and Scale end to end data pipelines from ingestion to transformation across batch and Streaming systems.
• Processing billions of sensor, simulation and telemetry events daily.
• Partner with AI, ML, and simulation teams to deliver ML-ready, traceable, and compliant datasets.
• Work hands-on with tools like Airflow, Spark, Databricks, and cloud platforms such as GCP or Azure.
• Lead data governance initiatives: schema evolution, data cataloging, and proactive pipeline monitoring.
• Enable AI workflows with efficient data selection, annotation, labeling pipelines, and feedback loops.
• Design and enforce best practices around data observability and data protection

Requirements

• 3+ years’ experience building and running scalable, resilient data pipelines in production.
• Strong Python and SQL skills, plus hands-on cloud expertise (GCP, Azure, AWS).
• Experience with orchestration tools like Airflow, Dagster or Perfect2
• Experience with Apache Spark, Apache Flink, Kafka, Databricks, or similar data engineering tools.
• Solid understanding of data architecture trade-offs — latency, throughput, cost, and storage formats like Parquet and Avro.
• A passion for data quality, reproducibility, and building maintainable systems, not just “moving data.”
• Bonus Points
• Experience with sensor data, telemetry, or real-time time-series data.
• Familiarity with cloud data warehouse (Snowflake, BigQuery)
• Familiarity with ML/AI infrastructure like feature stores, labeling pipelines, or MLOps tooling.
• Knowledge of CI/CD, Infrastructure as Code( Terraform), containerization (Docker/Kubernetes), and versioning of data and pipelines.

Benefits

• Flexible working — hybrid in Gothenburg or fully remote within the EU
• Opportunity to influence the future of large-scale data engineering in industries like autonomous systems
• Join a highly collaborative team of ML, simulation, and data experts