About the Role

We are seeking a skilled Data Engineer to design, develop, and maintain reliable data pipelines and scalable data infrastructure. The successful candidate will help transform raw information from multiple operational systems into structured, accessible, and trustworthy datasets that support business intelligence, analytics, and machine learning.

Working closely with data analysts, data scientists, software developers, and cloud infrastructure specialists, you will contribute to the full data lifecycle, including ingestion, transformation, storage, quality assurance, and delivery. You will help ensure that data systems remain efficient, maintainable, and capable of supporting evolving business requirements.

This position is suited to an engineer with strong programming and SQL skills, an understanding of modern data architectures, and an interest in building dependable data solutions within Estonia’s technology sector.

Key Responsibilities

• Design, develop, and maintain batch and near-real-time data pipelines from internal applications, APIs, databases, and external data sources.
• Build data ingestion and transformation workflows using Python, SQL, and appropriate data processing frameworks.
• Develop and maintain data warehouse and lakehouse solutions that support reporting, analytics, and downstream applications.
• Implement ETL and ELT processes with appropriate validation, error handling, and recovery mechanisms.
• Design efficient database schemas, data models, and transformation logic for analytical workloads.
• Work with cloud data platforms and storage services to support secure, scalable, and cost-effective data processing.
• Monitor pipeline execution, investigate failures, and resolve performance bottlenecks.
• Establish data quality checks to identify missing records, duplicate entries, inconsistent values, and schema changes.
• Implement logging, monitoring, alerting, and operational documentation for production data workflows.
• Collaborate with analysts and data scientists to understand dataset requirements and improve data accessibility.
• Apply version control, automated testing, code review, and CI/CD practices to data engineering projects.
• Follow appropriate security, access control, and data governance requirements.

Requirements and Qualifications

• Degree or equivalent practical experience in computer science, software engineering, data engineering, or a related technical discipline.
• Demonstrable experience developing data pipelines or backend data processing systems.
• Strong proficiency in SQL and Python.
• Understanding of relational databases, data modelling, and query optimisation.
• Experience with ETL/ELT workflows, data warehouses, and analytical data structures.
• Familiarity with at least one cloud platform, such as AWS, Microsoft Azure, or Google Cloud.
• Knowledge of Git, automated testing, and software development best practices.
• Ability to troubleshoot complex data issues and communicate technical findings clearly.
• Strong analytical thinking, attention to detail, and collaborative working skills.
• Professional proficiency in English; Estonian language skills are an advantage.

Preferred Experience

Experience with Apache Airflow, dbt, Apache Spark, Kafka, Databricks, Snowflake, BigQuery, or Microsoft Fabric is desirable, depending on the organisation’s technology stack. Familiarity with Docker, infrastructure automation, and containerised workloads will also be beneficial.

What We Offer

• The opportunity to build and improve modern data infrastructure.
• Exposure to cloud-native engineering, scalable processing, and analytical data platforms.
• Collaboration with data science, analytics, and software engineering professionals.
• Opportunities to expand expertise in distributed systems and data architecture.
• Competitive remuneration based on experience and technical capabilities.

Ideal Candidate

The ideal candidate is a practical problem-solver who values reliable systems, clean code, and well-structured data. You should be comfortable investigating pipeline failures, improving processing efficiency, and designing solutions that remain maintainable as data volumes and business requirements grow.

Are you interested in this position?

Apply by clicking on the “Apply Now” Button below!

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