{"id":2093545,"url":"https://alion.io/job/svitla-systems-data-engineer-2","title":"Data Engineer","company":{"id":2216,"name":"Svitla Systems","domain":"svitla.com","url":"https://alion.io/company/svitla-systems","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"A","score":88,"open_postings":4,"ghost_share":0,"stale_share":0.5,"repost_share":0,"time_to_fill_p50_days":49,"computed_at":"2026-10-10T05:45:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"inferred_payroll_markers","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["DE","FR","GB","AT","BE","BG","HR","CZ","DK","FI","HU","IE","IT","NL","NO","PL","RO","ES","SE","CH","EE","GR","IS","LV","LI","LT","LU","MT","PT","SK","SI"],"hiring_countries_total":31,"salary":null,"salary_estimate":{"min_usd":72000,"max_usd":168000,"period":"year","method":"global_role_seniority_cell","sample_n":778},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-08T15:16:52Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-12T02:10:41Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Svitla Systems Inc. is looking for a Data Engineer for a full-time position (40 hours per week) in Europe. Our client is one of the world’s largest and fastest-growing job sites.\nWe are looking for a Data Engineer to work embedded within the data engineering function, partnering closely with the data platform team to support a large-scale data platform modernization effort. The role centers on migrating approximately 1,000 datasets to Snowflake, with Apache Airflow 3 handling orchestration. The core architecture and technical approach are already defined by the client's team, so the focus is on execution rather than design.\nThe overarching objective is to accelerate a set of time-sensitive migrations (Airflow 2 to 3, Confluent region migration, and the move to Iceberg), targeted for completion by the end of March 2027.\nCoverage until 12:00 PM PT\nRequirements\n3+ years of industry experience working with large-scale datasets, including Airflow and Spark.\nStrong software engineering fundamentals.\nHands-on Python development experience.\nAdvanced SQL, including complex joins, CTEs, window functions, and performance tuning.\nSnowflake data modeling experience.\nExperience developing and testing pipelines in Apache Airflow and Python.\nExperience with Apache Kafka.\nExperience with Apache Spark, including hands-on PySpark.\nExperience with Iceberg and Parquet data formats.\nAWS experience, particularly S3 (data lakes, partitioning, and lifecycle policies).\nTesting strategy experience, with the ability to debug and test code hands-on.\nStrong communication skills.\nAbility to work independently.\nWill be a plus\nHands-on experience with Snowflake migrations (data warehouse modernization, performance and cost optimization).\nExperience with Apache Airflow migrations (version upgrades, executor changes, or platform migrations).\nExperience using AI tools for development.\nResponsibilities\nCarry out the migration of approximately 1,000 datasets to Snowflake, with Apache Airflow 3 handling orchestration.\nMigrate a set of pipelines from Apache Airflow 2 to Airflow 3.\nMigrate Confluent connections to a new region.\nMigrate a set of pipelines from Hive to Iceberg using Snowpark Connect.\nAdapt existing data pipelines to Snowflake while ensuring data quality and reliability throughout.\nSupport workflow scheduling and monitoring in Apache 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