{"id":1191749,"url":"https://alion.io/job/randstad-hungary-senior-data-engineer","title":"Senior Data Engineer","company":{"id":2215937,"name":"Randstad Hungary","domain":"randstad.hu","url":"https://alion.io/company/randstad-hu","size_band":"1001-5000","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Traffit","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Git","optional":false},{"name":"IAM","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Apache Iceberg","optional":true},{"name":"Claude Code","optional":true},{"name":"Cursor","optional":true},{"name":"Dimensional Modeling","optional":true},{"name":"Embeddings","optional":true},{"name":"LLM","optional":true},{"name":"RAG","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-09-11T05:49:56Z","employer_posted_date":"2026-09-11","last_verified_at":"2026-09-25T23:05:51Z","board_verified":true,"closed_at":null,"days_open":14,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":14},"description":"High Technical Impact: A unique professional challenge to shape the core data platform for a stable, market-leading global enterprise.\n\nCompetitive Compensation: Senior level base salary and annual target bonus.\n\nBenefits Package: Cafeteria allowance and private health insurance package.\n\nFlexibility: Hybrid working model requiring 3 days of office presence (Budapest) and offering 2 days of Home Office per week.\n\nResponsibilities\nPipeline Engineering & Architecture: Design, build, and maintain high-performance, end-to-end data pipelines covering ingestion, processing, quality assurance, and delivery layers. Own critical data architecture decisions and technical implementations.\n\nLakehouse & Platform Optimization: Optimize data platform performance, reliability, and cost across Databricks Lakehouse architectures. Apply advanced tuning techniques (partitioning, clustering, materialized views, pre-aggregation) and implement medallion patterns (bronze/silver/gold with published consumer layers).\n\nML/AI Operations Enablement: Partner closely with data science, analytics, and ML/AI operations teams to build platforms and pipelines that enable model development, training, feature engineering, and production deployment.\n\nData Governance & Observability: Establish and drive best practices around data quality assurance, governance, metadata management, validation frameworks, and platform observability.\n\nInfrastructure & CI/CD: Manage infrastructure as code and CI/CD practices across Git-based deployment workflows.\n\nTechnical Leadership & Innovation: Influence technical direction, mentor junior engineers, evaluate and adopt emerging technologies, and effectively partner across both technical and non-technical stakeholders.\n\nRequirements\nRequired Qualifications\nExperience: 10+ years of professional data engineering experience building production-grade data systems.\n\nCore Stack: Deep expertise with Databricks and Apache Spark, with proven experience optimizing complex distributed workloads.\n\nAWS & Lakehouse: Production experience with the Databricks Lakehouse on AWS (Unity Catalog, Delta Lake, Delta Live Tables, Databricks SQL) alongside core supporting AWS services (S3, IAM, Lambda, RDS).\n\nLanguages & Querying: Expert SQL skills and strong programming proficiency in Python (PySpark). (Scala knowledge is a plus).\n\nArchitectural Mastery: Solid understanding of Lakehouse and data warehouse architectures, medallion patterns, and dimensional modeling.\n\nData Domain Background: Professional experience at companies where data ingestion, delivery, and consumption are core business functions.\n\nCollaboration & Leadership: Strong communication skills with a proven ability to mentor junior engineers, influence technical direction, and partner across diverse stakeholders.\n\nPreferred Qualifications\nHands-on experience with data science workflows (feature engineering, model input preparation, evaluation support) and production ML/AI operations (ML monitoring, retraining pipelines, model deployment).\n\nExposure to LLM/generative AI infrastructure, RAG systems, or embeddings pipelines in production.\n\nInfrastructure-as-Code and CI/CD proficiency (Databricks Asset Bundles, Terraform, Git-based workflows).\n\nAdvanced knowledge of open table formats (Delta Lake, Apache Iceberg), data quality frameworks, metadata management, and observability/monitoring tools.\n\nCloud cost optimization expertise and/or contributions to open-source data engineering projects.\n\nTechnical Skills & Domain Knowledge\nDistributed computing fundamentals, large-scale data processing optimization, and performance benchmarking/profiling.\n\nReal-time streaming, event-driven architectures, API design, and integration patterns for data consumption.\n\nSecurity, encryption, and compliance requirements for sensitive enterprise data.\n\nFamiliarity with AI-assisted development tools (Claude Code, Cursor, Databricks Genie).","description_format":"text","description_chars":3918,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Cafeteria","Health insurance","Home office","Hybrid work"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-24T17:00:12Z"}],"liveness":{"score":73,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.806,"p_room":0.9,"age_days":13,"expected_fill_days":32,"reasons":["conf:1","win:mid"],"computed_at":"2026-09-25T05:45:01Z"},"pay":null,"html_url":"https://alion.io/job/randstad-hungary-senior-data-engineer","json_url":"https://alion.io/job/randstad-hungary-senior-data-engineer.json","meta":{"generated_at":"2026-09-26T01:20:26Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1305,"day_limit":5000,"remaining_today":3695,"minute_limit":60,"resets_at":"2026-09-27T00:00:00Z"}}}