Senior Data Engineer

🔥 0 minutes ago

🇵🇱 Poland – Remote

⏰ Full Time

🟠 Senior

🚰 Data Engineer

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

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 • Design and implement near-real-time ingestion pipelines using event-driven patterns • Define and enforce data platform standards, including Data Lake and Lakehouse principles, medallion architecture, and data contracts • Refactor, optimize, and modernize 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, scalability, and reliability across all platforms and pipelines • Design and deliver self-service tooling and microservices that simplify platform usage • Collaborate with Product, Machine Learning, and Data Science teams • Contribute to architecture decisions and technical R&D initiatives

🎯 Requirements

• 5+ years of professional experience in Data Engineering • Strong Python and SQL development skills • Hands-on experience with Apache Spark and PySpark, including query optimization and performance tuning • Experience working with Databricks or Snowflake • Practical experience with at least one major cloud provider such as Azure, AWS, or GCP • Experience with stream processing technologies including Kafka or Spark Structured Streaming • Strong understanding of ETL/ELT patterns, data modelling, and data warehousing concepts • Experience with orchestration tools such as Apache Airflow or Azure Data Factory • Knowledge of Infrastructure as Code tools including Terraform • Understanding of production-grade systems, including observability, scalability, reliability, and performance • Ability to independently lead technical initiatives from concept to delivery • Strong communication and collaboration skills • Upper-Intermediate or higher English level • Familiarity with RAG pipeline design and LLM integration patterns • Knowledge of data governance frameworks and tools such as Unity Catalog or Apache Atlas • Experience with dbt for data transformation and modelling • Familiarity with MLflow, Feature Stores, or ML platform integrations

🏖️ Benefits

• Remote work option • Opportunity to work on high-impact, greenfield data initiatives • Exposure to modern cloud-native data platforms and AI-ready data infrastructure • R&D opportunities involving agentic AI architectures, event-driven systems, and LLM-ready data pipelines • Cross-functional collaboration with Machine Learning, Data Science, and Product teams • Knowledge-sharing and team-oriented environment