Mid/Senior Data Engineer

We are looking for a Mid/Senior Data Engineer to join our clients team responsible for building and evolving data engineering solutions supporting risk and finance reporting. In this role, you will help integrate data across multiple systems, develop scalable data models, and build solutions that support evolving regulatory and business requirements. You will play a key role in designing data architecture, developing reliable data pipelines, and establishing best practices for modern data engineering. Looking for a senior analyst as priority. They need a person to analyze and remap Navision data exports into Business central and loading into Dim/Fact model. Company offer: Project/Client: Banking Location: Remote (Baltic States, EU) Language: English Engagement: Full-time B2B contract till the end of the year (possibility to renew) Hourly rate: 35-45 EUR Key Responsibilities Design, develop, maintain, and enhance data ingestion, processing, storage, and sharing solutions. Build and optimize data architecture to ensure scalability, reliability, and high performance. Develop and maintain ETL/ELT pipelines using modern data engineering tools and platforms. Ensure seamless integration and synchronization of data across multiple systems. Maintain high standards of data quality, security, availability, and performance. Collaborate with business analysts, software engineers, and stakeholders to understand data requirements and deliver effective solutions. Participate in code reviews, troubleshoot software issues, and implement improvements. Build and maintain monitoring and alerting solutions for data pipelines. Continuously expand knowledge of financial processes, data management, and reporting practices. Work within an international Agile team. Requirements 3–5+ years of experience in Data Engineering or a similar role (experience within the financial services industry is considered a strong advantage). Strong programming skills in SQL and Python ; experience with additional ETL tools or platforms is a plus. Hands-on experience working with both SQL and NoSQL databases. Experience with at least one major cloud platform ( AWS , Azure , or GCP ). Experience with containerization and orchestration technologies such as Docker and Kubernetes . Good understanding of data streaming technologies, including Kafka , Spark , or Flink . Experience with workflow orchestration tools such as Airflow or dbt . Experience working with modern data warehouse technologies such as Snowflake , BigQuery , or Amazon Redshift . Knowledge of system integration approaches, including real-time, message-based, and event-driven architectures. Understanding of test automation, DevOps practices, Infrastructure as Code (IaC), and security best practices. Experience within risk management or finance-related business domains is a strong advantage. Ability to work independently, take ownership, and proactively drive initiatives. Excellent written and spoken English communication skills. What They Offer Opportunity to work on modern, large-scale data engineering solutions. Exposure to cloud-native technologies and modern data platforms. Collaboration with an experienced international Agile team. The chance to contribute to business-critical data solutions in a complex, fast-paced environment. Recruitment process: CV Screening: Applications are reviewed within 24 hours. Pre-Screening Interview: A brief Q/A session ( Automated or with a Recruiter ) designed to learn more about your experience related to the required job position. Automated Session (Recommended) – You can complete this session on your own at a time that is convenient for you. The questions and follow-ups are well-structured and designed to highlight your experience and provide detailed insights into your background. This option is recommended because it's usually more detailed and allows us to provide feedback from the hiring manager faster. Session with a Recruiter – You can also have the session with a recruiter. The questions are the same, but the discussion may be a bit less detailed, and feedback might take a little longer. Shortlisting: Qualified candidates are presented to the hiring manager for review. Formal Interviews: Online discussions with the hiring manager or project team, with feedback provided within 1-2 weeks. Offer and Onboarding: Successful candidates receive a formal offer and begin a structured onboarding process.  Information about the processing of your personal data is provided in our Privacy Policy, which is available online a Privacy Policy