Data Engineering & Architecture

• Design, build, and maintain scalable ETL/ELT pipelines to ingest, transform, and deliver Finance data from multiple source systems into Snowflake
• Implement data blending strategies to consolidate data across heterogeneous sources (ERP systems, flat files, APIs, databases) into unified, trusted datasets
• Apply Snowflake-native capabilities: dynamic tables, streams, tasks, materialized views, data sharing, and time travel
• Model data using dimensional and modern approaches (e.g. Data Vault, medallion architecture) appropriate to Finance use cases
• Ensure data quality, consistency, and lineage through robust testing and monitoring practices
• Optimize query performance and cost efficiency within Snowflake

Pipelines & Orchestration

• Build and maintain data pipelines using orchestration tools such as Apache Airflow, dbt, or equivalent
• Implement CI/CD practices for data pipelines using GitHub Actions or similar, ensuring version-controlled, reviewable, and deployable data code
• Monitor pipeline health, set up alerting, and own incident resolution for data failures

Backend & API Development

• Design and develop RESTful APIs to expose Finance data to internal web applications and downstream consumers
• Collaborate with frontend and full-stack developers to define data contracts and ensure reliable, performant backend integration
• Implement authentication, authorization, and data access governance appropriate for sensitive Finance data

Cloud & Infrastructure

• Work within cloud environments (AWS) and apply cloud data warehouse best practices around security, scalability, and cost management
• Contribute to infrastructure-as-code practices where relevant (Terraform or equivalent)
• Champion data security and compliance standards, particularly around financial data sensitivity and regulatory requirements

Collaboration & Standards

• Partner with Finance domain experts and analysts to understand data needs and translate them into technical solutions
• Contribute to and enforce data engineering standards, documentation, and best practices across the team
• Mentor junior engineers and act as a technical reference point within the domain

• 5+ years of experience in data engineering, with at least 2 years working extensively with Snowflake in a production environment
• Strong hands-on experience with ETL design, data blending, and complex data transformation logic
• Proficiency in SQL (advanced level) and Python for data engineering tasks
• Experience building and maintaining data pipelines with orchestration tools (Airflow, dbt, Prefect, or similar)
• Solid understanding of cloud data warehouse concepts: partitioning, clustering, materialized views, query optimization, and cost governance
• Experience working with Finance data (general ledger, cost center structures, P&L, FP&A, or similar domains) is a strong advantage
• Proficiency with GitHub for version control, branching strategies, and collaborative code review
• Experience developing and documenting backend REST APIs
• Familiarity with CI/CD pipelines for data workloads

Nice to Have

• Experience with SAP (S/4HANA, BW4) or other ERP source systems as data origins
• Knowledge of data governance frameworks and metadata management tools (e.g. Collibra, DataHub)
• Exposure to Snowflake’s data sharing and Marketplace capabilities
• Familiarity with dbt (data build tool) for transformation and testing
• Understanding of dimensional modeling
  Experience in regulated industries (pharma, finance, life sciences)

Tech Stack

• Data Platform: Snowflake
• Orchestration: Apache Airflow / dbt
• Languages: Python, SQL
• Version Control & CI/CD: GitHub, GitHub Actions
• Cloud: Azure / AWS
• API: REST, FastAPI or equivalent
• BI & Consumption Layer: Tableau or equivalent

• Participation in interesting and demanding projects.
• Flexible working hours.
• A great, non-corporate atmosphere.
• Possibility to work remote or hybrid (2 days per week from the office).
• Opportunities for development and promotion.
• Attractive package of benefits.