Job Overview
Location
Europe Remote
Job Type
Full-time
Category
Software Engineering
Date Posted
June 25, 2026
Full Job Description
đź“‹ Description
- • Build and maintain production-grade data pipelines in Databricks using SQL, Python, and PySpark.
- • Implement ELT/ETL patterns for both batch and streaming data processing workflows.
- • Develop and maintain Lakehouse data models and curated datasets aligned with data warehousing best practices (Kimball, Inmon, Data Vault).
- • Utilize Databricks-native capabilities including Lakeflow Declarative Pipelines (DLT) to create robust, maintainable data pipelines.
- • Implement data quality checks and monitoring using Expectations to ensure reliability and trust in data products.
- • Configure and manage governance and access controls via Unity Catalog, including catalog/schema management, permissions, and lineage-friendly practices.
- • Optimize pipeline performance and cost through cluster sizing, partitioning, file size tuning, caching, and query optimization.
- • Collaborate with analytics, data science, and engineering teams to translate business requirements into well-defined data contracts and deliverables.
- • Create and maintain technical documentation for pipelines, data models, and operational runbooks.
- • Support operational excellence through incident response, root-cause analysis, and continuous improvement of data platform reliability.
- • Adopt an ownership mindset: build it, run it, and ensure long-term sustainability of data systems.
- • Apply pragmatic engineering principles focused on reliability, clarity, and maintainability over overly complex or “clever” solutions.
- • Communicate effectively with stakeholders to align on data definitions, assumptions, and trade-offs across teams.
🎯 Requirements
- • Proven, hands-on Databricks experience in production environments.
- • Strong working knowledge of SQL, Python, and PySpark for data engineering workloads.
- • Practical experience with Databricks-specific technologies: Lakeflow Declarative Pipelines (DLT), Expectations/data quality validation, and Unity Catalog (governance, access control, catalog/schema management).
- • Solid experience with data warehousing design and modeling methodologies (Kimball, Inmon, or Data Vault).
- • Understanding of data engineering fundamentals: orchestration patterns, incremental processing, SCDs, metadata management, and observability.
🏖️ Benefits
- • A Truly Global Workplace – work with professionals from 40+ nationalities in a collaborative international culture.
- • Hybrid & Flexible Work – remote work options and modern office spaces across Europe.
- • A Culture of Growth – access to LinkedIn Learning, mentorship, and professional development programs including HiPo and leadership initiatives.
- • Financial Growth Opportunities – share purchase matching programme with matched contributions for long-term financial rewards.
- • Workation Programme – work remotely from different countries for up to 2 months per year.
Skills & Technologies
See exactly how your profile matches this role — strengths, skill gaps, and what to do about them.
About Multitude AG
Multitude AG is a fintech group headquartered in Finland that provides digital lending, payment, and banking technology services. Through brands such as Ferratum, CapitalBox, and SweepBank, it offers consumer and small-business loans, white-label banking platforms, and payment processing solutions across Europe, the Americas, and Asia-Pacific. The company operates on a regulated banking-as-a-service model, combining proprietary technology with risk management services to enable partners to launch financial products quickly and compliantly.
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