Senior Data Engineer

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Sigma Software Group

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2002

💼 Consulting

🏥 Healthcare

🚘 Automotive

Consulting • Healthcare • Automotive

Sigma Software Group is a multinational company, established in 2002, that specializes in providing high-quality software development, graphic design, testing, and support services. The company focuses on delivering solutions across various industries such as automotive, telecommunications, aviation, advertising, gaming, banking, real estate, and healthcare. Sigma Software values professional growth, offers remote work opportunities worldwide, and caters to world-renowned clients like AstraZeneca, Scania, and SAS. The company emphasizes a culture of continuous education, mentorship, and flexible work environments, making it a preferred workplace for IT specialists aiming to work on complex solutions utilizing cutting-edge technologies. Sigma Software is committed to innovative solutions and engineering the future while also contributing to social causes such as charitable work in Ukraine.

📋 Description

• Design and build scalable, cloud-native data platforms from greenfield to production • Implement near-real-time ingestion pipelines using event-driven patterns • Define and enforce platform standards, including Data Lake / Lakehouse principles, medallion architecture, and data contracts • Refactor and optimise existing Spark and PySpark scripts for performance and maintainability • Introduce best practices for code quality, testing, and CI/CD across data pipelines • Drive adoption of AI tooling and agentic workflows within the data engineering team • Ensure data quality, observability, and reliability across all pipelines and platforms • Develop self-service tooling and microservices to simplify platform usage for other teams • Collaborate with Machine Learning, Data Science, and Product teams as a key technical contributor and thought leader • Drive R&D efforts around agentic AI architectures, event-driven systems, and LLM-ready data pipelines, turning architectural concepts into production-grade solutions • Build modern cloud-native data platforms, migrate on-premises legacy systems to the cloud, and establish AI-ready data infrastructure

🎯 Requirements

• 5+ years of professional experience in Data Engineering • Strong Python and SQL development skills for pipeline development and optimisation • Proficiency in Apache Spark / PySpark, including query optimisation and performance tuning • Hands-on experience with Databricks (preferred) or Snowflake • Experience with at least one major cloud provider: Azure (preferred), AWS, or GCP • Experience with stream processing technologies (Kafka, Spark Structured Streaming) • Solid understanding of ETL/ELT patterns, data modelling (dimensional, Data Vault), and data warehousing • Experience with orchestration tools (Apache Airflow, Azure Data Factory, or equivalent) • Knowledge of Infrastructure as Code (Terraform or equivalent) • Understanding of production-grade system requirements: reliability, scalability, observability, and performance • Upper-Intermediate English level • Familiarity with RAG pipeline design and LLM integration patterns • Knowledge of data governance frameworks and tools (Unity Catalog, Apache Atlas, or similar) • Experience with dbt for data transformation and modelling • Familiarity with MLflow, Feature Stores, or ML platform integration

🏖️ Benefits

• Employees can work remotely • Full-time employment