{"id":2365239,"url":"https://alion.io/job/sigma-software-group-data-engineer-3","title":"Data Engineer","company":{"id":6266,"name":"Sigma Software Group","domain":"sigma.software","url":"https://alion.io/company/sigma-software-group","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Kyiv, Ukraine"],"countries":["UA"],"hiring_countries":["UA"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":26000,"max_usd":71000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":782},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Databricks","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Agile","optional":true},{"name":"Amazon Redshift","optional":true},{"name":"Apache Kafka","optional":true},{"name":"BigQuery","optional":true},{"name":"CI/CD","optional":true},{"name":"Delta Lake","optional":true},{"name":"Git","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-10-12T14:32:00Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-13T00:23:45Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"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.\nResponsibilities\nDesign and develop scalable and reliable data platforms and/or data pipelines for analytical and operational use cases\nDesign and implement data processing workflows using distributed processing frameworks such as Apache Spark/pySpark (Databricks)\nContribute 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)\nStrong background in data modelling to support analytics, reporting, and downstream data consumption\nIntegrate and process data from multiple internal and external sources while ensuring data consistency, quality, and reliability\nTake ownership of data pipeline reliability, monitoring, and performance, ensuring scalable and maintainable data processing workflows\nCollaborate with data analysts, data scientists, and engineering teams to translate business and analytical requirements into robust data solutions\nDrive improvements in data engineering practices, including pipeline design standards, testing approaches, and operational reliability\nParticipate in evaluating and introducing new tools and technologies within the data engineering ecosystem\n Required Qualifications\nStrong hands-on experience (4+ years) in data engineering or building data processing platforms\nStrong SQL skills and solid experience with Python for building and maintaining data pipelines\nExperience working with distributed data processing frameworks such as Apache Spark/pySpark (Databricks experience is a strong advantage)\nPractical experience designing and implementing data pipelines in cloud environments, preferably within the Azure ecosystem\nExperience designing data pipelines and data models for production-scale analytical systems\nStrong understanding of data engineering concepts including ETL/ELT design, dimensional modeling, and data warehousing principles\nExperience working with modern data lake architectures and data formats such as Parquet and JSON\nExperience with workflow orchestration tools (e.g., Airflow)\nExperience with database development and performance optimization\nAbility to design scalable and maintainable data solutions with minimal supervision\nStrong collaboration and communication skills\nProfessional working proficiency in English\nNice to Have\nExperience with modern cloud data warehouses such as Snowflake, BigQuery, or Redshift\nExperience with streaming data technologies (e.g., Event Hub, Kafka, Spark Streaming)\nExperience with Delta Lake or other lakehouse technologies\nFamiliarity with CI/CD practices and Git-based development workflows\nExperience working in agile development 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