Staff Engineer — Data Platform
New
Belgium, France, Germany, Italy, Netherlands, Portugal, Spain, Sweden, United KingdomFull-TimeStaff
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 8+ years
- Required Skills
- PythonSQLKafkaData engineeringSparkData modeling
Requirements
- 8+ years of experience in data engineering, software engineering, or a related field.
- At least 2 years operating at a staff or principal level.
- Deep expertise in designing and building large-scale data platforms using streaming, batch, or hybrid architectures.
- Hands-on experience with Spark, Flink, Kafka, StarRocks, or equivalent.
- Strong Python and SQL skills.
- Solid understanding of data modeling techniques such as dimensional modeling, Data Vault, or lakehouse patterns.
- Experience with cloud data infrastructure (AWS, GCP, or Azure).
- Strong grasp of data quality, observability, and governance principles.
- Proven ability to set standards and lead technical initiatives across multiple teams.
- Professional proficiency in English — written and spoken.
- Experience in the payments or fintech industry (preferred).
- Familiarity with dbt, Great Expectations, or similar (preferred).
Responsibilities
- Define architecture within the data platform, structure and deliver projects and initiatives end-to-end.
- Act as a technical reference point for the Data team, setting quality standards, testing, observability, data modeling, and documentation.
- Lead the design and implementation of scalable, low-latency data pipelines that process high-volume payment transactions in real time.
- Champion an AI-first engineering culture, establishing standards for AI-assisted development, automated data quality testing, and LLM-powered data workflows.
- Design and build performant and reliable data pipelines for large volumes of payment data.
- Design scalable data models supporting fraud detection, revenue analytics, and regulatory reporting.
- Own platform reliability including SLAs, data quality, alerting, and incident response.
- Partner with Product, AI, and Finance teams to translate business needs into scalable data solutions.
- Mentor senior and mid-level engineers through code reviews, design reviews, and knowledge-sharing.
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