{"id":3466341,"url":"https://alion.io/job/ralabs-senior-data-engineer","title":"Senior Data Engineer","company":{"id":2058105,"name":"Ralabs","domain":"ralabs.org","url":"https://alion.io/company/ralabs","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Ukraine"],"countries":["UA"],"hiring_countries":["UA"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":61000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2679},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Aurora","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Node JS","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"JavaScript","optional":true},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-10-13T15:00:16Z","employer_posted_date":null,"last_verified_at":"2026-10-13T15:00:16Z","board_verified":false,"closed_at":null,"days_open":18,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":18},"description":"We are looking for a Senior Data Engineer to join a project in the tech capital of the world – Silicon Valley.\n\n17:00–02:00 Kyiv time = 7:00–16:00 Pacific Time (PST/PDT).\n\nProject Idea\n\nThe project was founded back in 2014 with the goal of connecting private and government universities with regular people like we are. You have a variety of auditoriums, gyms, classrooms, and other venue options available for community use –\n\nschedule facility uses and manage requests from the community all in one place.\n\nJust imagine that you’re a football player and you can rent a football field at Harvard to play with your friends. Amazing, right?\n\nMust have:\n\n5 + years of experience in Data Engineering or related roles;\n\nStrong SQL skills and hands-on experience with PostgreSQL (including Aurora Serverless);\n\nSolid knowledge of Python for data processing and automation;\n\nExperience building and maintaining ETL/ELT pipelines using cloud-native tools (e.g., AWS Lambda, S3, SQS);\n\nProven experience working with MongoDB and MongoDB Atlas, including event-driven architectures using Atlas Triggers, Stream Processing;\n\nProficiency with dbt for building modular, testable, and well-documented data transformation workflows;\n\nGood understanding of data modeling principles for OLAP/OLTP systems, including normalization and dimensional modeling;\n\nDemonstrated experience designing and implementing data warehouses and data marts;\n\nWorking knowledge of Node.js, particularly in backend logic tied to data ingestion or transformation workflows;\n\nFamiliarity with cloud data platforms (e.g., AWS) and serverless computing patterns;\n\nA technical degree (e.g., Computer Science, Engineering, Math) is a plus;\n\nUpper-Intermediate English or higher for effective communication and documentation.\n\nSoft skills:\n\nProactive – you take ownership and act without waiting for direction;\n\nDetail-oriented – you deliver accurate, high-quality work;\n\nInitiative-driven – you’re eager to improve processes and take action.\n\nResponsibilities:\n\nDesign, implement, and maintain scalable and reliable data pipelines using Python, dbt, and AWS Lambda;\n\nBuild and optimize data architectures to support analytics, reporting, and machine learning use cases, including data warehouse and data mart modeling on PostgreSQL (Aurora Serverless);\n\nDevelop and manage ELT workflows that extract data from MongoDB (using Atlas triggers) and load into staging and production layers in PostgreSQL;\n\nEnsure data consistency and lineage by applying robust data quality checks, auditing, and reconciliation logic;\n\nCollaborate with cross-functional teams to gather data requirements, understand business logic, and translate them into efficient data models and transformations;\n\nMonitor and troubleshoot SQS/Lambda-based ingestion pipelines, addressing issues related to concurrency, message processing, and data duplication;\n\nContribute to the semantic layer design used by BI and reporting tools to ensure consistency and accessibility of business metrics;\n\nMaintain and evolve dbt models (staging, intermediate, and marts) aligned with software engineering and analytics best practices;\n\nDrive continuous improvement in data engineering processes and data governance standards, ensuring scalability, maintainability, and 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