Data Engineer
Remote Kyiv Full-time Mid-level Data Engineering
At a glance
Mid-level Data Engineering role at Sigma Software. Remote, Ukraine · full-time.
Pay not stated Data Engineer salary
RoleData Engineering
SeniorityMid-level
LocationKyiv
WorkplaceRemote
EmploymentFull-time
PostedOct 12, 2026
Role brief
remoteroles summary, based on the employer's posting.
What you'll do
- Build data platforms and pipelines for analytics and operational work, keeping processing dependable as projects scale
- Create distributed data workflows with Spark or PySpark, including Databricks where relevant
- Develop the cloud data platform with Azure services and connect data from internal and external sources
- Model data for reporting and downstream use, while maintaining data quality, reliability, and performance
- Work with analysts, scientists, and engineers to turn requirements into solutions and improve pipeline standards and testing
What you bring
- At least four years building data engineering solutions or data processing platforms
- Strong SQL and Python skills for developing and maintaining data pipelines
- Hands-on experience with Spark or PySpark and production-scale pipelines and data models
- Practical cloud pipeline experience, preferably with Azure, plus knowledge of ETL/ELT, dimensional modeling, and warehousing
- Professional working English and the ability to design maintainable solutions with little supervision
Who this fits
This role suits a mid-level data engineer comfortable owning production pipelines and collaborating across data and engineering teams. The position is remote and based in Kyiv.
From the employer
We are looking for a Data Engineer to help design and build scalable data platforms that support analytics, reporting, and data-driven decision-making across the organization. In this role, you will develop robust data pipelines, contribute to the evolution of our cloud data platform, and ensure reliable and efficient data processing across multiple projects and teams.
Responsibilities
- Design and develop scalable and reliable data platforms and/or data pipelines for analytical and operational use cases
- Design and implement data processing workflows using distributed processing frameworks such as Apache Spark/pySpark (Databricks)
- Contribute to the evolution of the organization’s cloud data platform using Azure technologies (e.g., Azure Data Factory, Synapse, Microsoft Fabrik, Databricks, Event Hub, Azure Data Lake Storage)
- Strong background in data modelling to support analytics, reporting, and downstream data consumption
- Integrate and process data from multiple internal and external sources while ensuring data consistency, quality, and reliability
- Take ownership of data pipeline reliability, monitoring, and performance, ensuring scalable and maintainable data processing workflows
- Collaborate with data analysts, data scientists, and engineering teams to translate business and analytical requirements into robust data solutions
- Drive improvements in data engineering practices, including pipeline design standards, testing approaches, and operational reliability
- Participate in evaluating and introducing new tools and technologies within the data engineering ecosystem
Required Qualifications
- Strong hands-on experience (4+ years) in data engineering or building data processing platforms
- Strong SQL skills and solid experience with Python for building and maintaining data pipelines
- Experience working with distributed data processing frameworks such as Apache Spark/pySpark (Databricks experience is a strong advantage)
- Practical experience designing and implementing data pipelines in cloud environments, preferably within the Azure ecosystem
- Experience designing data pipelines and data models for production-scale analytical systems
- Strong understanding of data engineering concepts including ETL/ELT design, dimensional modeling, and data warehousing principles
- Experience working with modern data lake architectures and data formats such as Parquet and JSON
- Experience with workflow orchestration tools (e.g., Airflow)
- Experience with database development and performance optimization
- Ability to design scalable and maintainable data solutions with minimal supervision
- Strong collaboration and communication skills
- Professional working proficiency in English
Nice to Have
- Experience with modern cloud data warehouses such as Snowflake, BigQuery, or Redshift
- Experience with streaming data technologies (e.g., Event Hub, Kafka, Spark Streaming)
- Experience with Delta Lake or other lakehouse technologies
- Familiarity with CI/CD practices and Git-based development workflows
- Experience working in agile development environments
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