Senior Data Engineer (Databricks Migration)
At a glance
Senior Data Engineering role at Sigma Software. Remote, Ukraine · full-time.
Pay not stated Data Engineer salary
remoteroles summary, based on the employer's posting.
What you'll do
- Move the analytics platform from BigQuery to Databricks, choosing migration paths for existing ETL and ELT workloads
- Build Lakehouse structures with Databricks and Delta Lake, including Bronze, Silver and Gold layers
- Create and tune PySpark pipelines for high-volume retail and analytics data, including incremental processing
- Apply Unity Catalog governance and security practices, and develop reusable platform components
- Review code, improve complex SQL and Spark workloads, and help keep production deployments reliable
What you bring
- At least five years working as a data engineer
- Strong Python and advanced SQL skills, with commercial Databricks work and substantial PySpark experience
- Experience building cloud data platforms and large-scale ETL or ELT processing, including hands-on Delta Lake work
- Upper-intermediate English or better, and the ability to work with international, cross-functional teams
Who this fits
This senior role suits an engineer who has built data platforms and can take part in architecture decisions as well as hands-on migration and pipeline work. You’ll collaborate with international teams across data science, analytics, product and customer engineering. The role is remote and based in Kyiv.
From the employer
Are you a Senior Data Engineer passionate about building scalable, high-performance data platforms and working with modern Lakehouse technologies? Join Sigma Software’s Data Engineering Center of Excellence and contribute to the modernization of an enterprise-scale analytics ecosystem for the retail domain.
We are looking for a Senior specialist with strong Databricks, PySpark, and cloud data engineering expertise to participate in the migration of a large-scale analytical platform from BigQuery to Databricks. You will collaborate with international teams, contribute to architectural decisions, and help shape reliable and scalable data solutions.
We at Sigma Software create opportunities for continuous learning, technology growth, and meaningful engineering impact while working on complex international projects.
CUSTOMER
Our Customer is a leading retail technology company specializing in AI-driven pricing optimization solutions for enterprise retailers. The company helps businesses improve profitability and competitiveness through advanced analytics, automation, and intelligent pricing strategies. Their platform combines business intelligence with sophisticated algorithms to support data-informed pricing decisions at scale for global retail organizations.
PROJECT
The project focuses on the strategic migration of a large-scale analytical platform from a legacy BigQuery ecosystem to a modern Databricks Lakehouse architecture. The platform processes high-volume retail datasets, machine learning workloads, analytics pipelines, and customer-specific business logic.
As part of the modernization initiative, the engineering team is implementing scalable Spark-based processing, Delta Lake architecture, medallion data layers, and modern governance practices. The role offers an opportunity to work with distributed data processing systems, optimize large-scale workloads, and contribute to the evolution of an enterprise-grade data platform.
Key Technologies: Databricks, Apache Spark, PySpark, Delta Lake, Python, SQL, Airflow, GCP, CI/CD, Unity Catalog
- Participate in the migration of a large-scale analytical platform from BigQuery to Databricks
- Design and implement scalable Lakehouse architectures using Databricks and Delta Lake
- Analyze existing ETL / ELT workloads and define migration approaches
- Develop and optimize data pipelines processing large volumes of retail and analytical data
- Implement incremental processing strategies and scalable transformation frameworks
- Build and maintain Spark-based data processing solutions using PySpark
- Design and maintain medallion architecture layers including Bronze, Silver, and Gold
- Implement data governance and security best practices using Unity Catalog
- Collaborate with Data Science, Analytics, Product, and Customer Engineering teams
- Participate in architecture discussions and technical solution design
- Develop reusable data platform components and engineering standards
- Conduct code reviews and contribute to platform reliability and maintainability
- Troubleshoot and optimize complex SQL and Spark workloads
- Support production deployments and platform modernization activities
- 5+ years of professional experience as a Data Engineer
- Strong programming skills in Python and advanced SQL
- Hands-on commercial experience with Databricks
- Strong knowledge of Apache Spark, primarily PySpark
- Experience designing and building modern cloud-based data platforms
- Experience developing ETL / ELT pipelines and large-scale data processing solutions
- Hands-on experience with Delta Lake
- Experience with Spark Declarative Pipelines
- Experience with cluster monitoring, metrics analysis, and performance optimization
- Strong understanding of distributed data processing architectures
- Solid understanding of data warehousing concepts and dimensional modeling
- Experience with Airflow or similar orchestration tools
- Experience optimizing complex analytical SQL workloads
- Experience implementing CI / CD practices for data engineering platforms
- Strong troubleshooting and performance optimization skills
- Ability to work collaboratively in cross-functional international teams
- Upper-Intermediate or higher English level
WILL BE A PLUS
- Experience working with GCP cloud services
- Experience with AWS or Azure cloud platforms
- Experience in retail analytics or pricing optimization domains
- Experience supporting machine learning or AI-related data workloads
- Experience with platform modernization and cloud migration initiatives
PERSONAL PROFILE
- Strong analytical and problem-solving mindset
- Proactive and ownership-driven approach
- Ability to work independently and collaboratively
- Good communication and stakeholder collaboration skills
- Passion for scalable data engineering and modern data platforms
- Interest in continuous learning and technology innovation
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