{"id":2258795,"url":"https://alion.io/job/gft-technologies-tech-lead-data-engineer","title":"Tech Lead Data Engineer","company":{"id":51689,"name":"GFT Technologies","domain":"gft.com","url":"https://alion.io/company/gft-technologies","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Kraków, Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":17400,"max":26700,"currency":"PLN","period":"month","gross":true,"usd_annual":81972},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false}],"status":"live","first_seen_at":"2026-09-23T00:00:00Z","employer_posted_date":"2026-09-23","last_verified_at":"2026-10-11T09:55:56Z","board_verified":true,"closed_at":null,"days_open":18,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":18},"description":"Type of contract: Employment contract\nSalary range: 17400 - 26700 PLN gross\nWhat will you do?\nJoin a strategic data transformation initiative focused on building and enhancing a large-scale data pipeline that feeds and reconciles inbound data from deal stores to consumer regulatory and settlement systems. You will work on modernizing enterprise-grade financial services platforms within a highly regulated environment, leveraging cloud-native technologies, big data solutions, and distributed streaming architectures. The role involves designing high-performance systems, improving data processing capabilities, and collaborating closely with technical and business stakeholders.\nYour tasks\nDesign and develop scalable data processing solutions using Spark and Databricks\nBuild and enhance data pipelines using PySpark, Python and Java\nDevelop high-performance, high-availability distributed systems\nImplement and maintain streaming solutions based on Kafka architectures\nDesign and optimize cloud-native solutions in Microsoft Azure\nSupport CI/CD processes and DevOps practices\nCollaborate with cross-functional Agile teams to deliver business requirements\nAnalyze complex technical flows and propose suitable technical solutions\nParticipate in system modernization and application transformation initiatives\nEnsure reliability, scalability and performance of data platforms\nYour skills\n7+ years of professional software engineering or data engineering experience\nStrong experience with Spark and Databricks\nHands-on experience with PySpark\nStrong Python programming skills\nGood Java development experience\nDeep knowledge of Microsoft Azure cloud technologies\nExperience with Kubernetes\nExperience designing and implementing Kafka-based streaming solutions\nExperience with DevOps practices and CI/CD pipelines\nExperience building distributed, high-throughput and low-latency systems\nStrong problem-solving and analytical skills\nAbility to work independently on complex technical challenges\nExcellent communication and stakeholder management skills\nExperience working in Agile teams\nNice to have\nExperience providing Level 3 (DEV) production support in a high-pressure environment\nWe offer\nHybrid work in our client's office (Kraków) 3 days a week\nWorking in a highly experienced and dedicated team\nBenefit package tailored to your needs (medical, sport, lunch subsidy, life insurance, etc.)\nOnline training and certifications\nAccess to e-learning platform\nSocial 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