{"id":2449941,"url":"https://alion.io/job/kpler-senior-data-engineer-4","title":"Senior Data Engineer","company":{"id":4764,"name":"Kpler","domain":"kpler.com","url":"https://alion.io/company/kpler","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":79,"open_postings":6,"ghost_share":0,"stale_share":0.833,"repost_share":0,"time_to_fill_p50_days":57,"computed_at":"2026-10-16T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Greece","Hungary","Cyprus"],"countries":["GR","HU","CY"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":29000,"max_usd":74000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2324},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"Java","optional":false},{"name":"PHP","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Datadog","optional":true},{"name":"FinOps","optional":true},{"name":"Time Series Forecasting","optional":true}],"status":"live","first_seen_at":"2026-10-13T12:19:42Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-16T21:46:21Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"About the Role \n\nAs a Senior Data Engineer in our Maritime & Logistics division, you will take technical ownership of the data pipelines and processing infrastructure that power real-time global trade intelligence. You will architect, build, and maintain high-throughput batch and streaming pipelines that ingest complex maritime feeds from global providers. Working closely with Product, SRE, and Data Science teams, your work will directly ensure the accuracy, freshness, and scalability of the data that underpins our flagship analytics platforms. \n\nKey Responsibilities\nArchitect, build, and optimize scalable data pipelines and stream-processing workflows using Python, Apache Spark, Kafka, and Airflow.\n\nMaintain and evolve data architecture across polyglot backend microservices (Python/FastAPI, PHP, Java/Scala) to support continuous analytics delivery.\n\nEnsure end-to-end data quality, freshness, and reliability by implementing automated validation, monitoring frameworks, and strict SLA tracking.\n\nProvision and manage cloud-native data infrastructure on AWS using Terraform, Docker, and automated CI/CD practices.\n\nCollaborate cross-functionally with Product Managers, SRE, Platform Engineers, and Data Scientists to translate complex domain needs into performant data products.\n\nDrive operational and cost efficiency across data infrastructure, optimizing cloud resource usage and pipeline performance.\n\nExperience & Background\nWhat you'll need (Must-haves)\n6+ years of professional software or data engineering experience building complex, production-grade data processing systems.\n\nStrong expertise in Python and hands-on experience with distributed data processing tools such as Apache Spark, Kafka, or Airflow.\n\nAdvanced knowledge of database design, SQL performance tuning, and data modeling across relational and NoSQL stores.\n\nProven experience managing cloud infrastructure on AWS, utilizing Terraform, Docker, and modern CI/CD pipelines.\n\nPractical experience writing clean, maintainable code in polyglot environments (Python, Java/Scala, or PHP).\n\nNice-to-haves\nExperience building RESTful APIs using Python frameworks like FastAPI.\n\nBackground handling high-throughput time-series, geospatial, or maritime logistics datasets.\n\nExposure to cloud FinOps, observability, and cost management tooling 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