{"id":1193019,"url":"https://alion.io/job/scalable-os-data-engineer","title":"Data Engineer","company":{"id":2681560,"name":"Scalable OS","domain":"scalableos.com","url":"https://alion.io/company/scalable-os","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Makati, Philippines"],"countries":["PH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":14500,"max_usd":36000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":431},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure Data Factory","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Fivetran","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-09-16T00:00:00Z","employer_posted_date":"2026-09-16","last_verified_at":"2026-09-25T15:20:04Z","board_verified":true,"closed_at":null,"days_open":10,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":10},"description":"The Data Engineer will design, build, and maintain modern data solutions that support analytics, AI, reporting, and business transformation. The role will focus on developing reliable and scalable data pipelines, integrating client source systems, maintaining data quality, and improving data platform performance. The ideal candidate combines strong technical data engineering skills with a consulting mindset, the ability to communicate effectively with both technical and non-technical stakeholders, and a strong commitment to quality, collaboration, and continuous improvement. JOB RESPONSIBILITIES Data Pipeline Architecture & DeliveryDesign and implement scalable ETL/ELT data pipelines using modern cloud data platforms.\nBuild medallion architecture solutions across Bronze, Silver, and Gold data layers.\nDevelop data pipelines using Spark notebooks, data pipelines, lakehouses, and related data engineering technologies.\nBuild reliable data foundations by connecting source systems, establishing data models, and implementing data quality standards.\nOptimize data pipelines for reliability, scalability, performance, and maintainability. Source System Integration\nBuild and maintain integrations with ERP, CRM, SaaS, and other source systems.\nDevelop reliable data workflows using integration platforms, APIs, native connectors, and other appropriate technologies.\nSupport both data ingestion and reverse ETL processes where required.\nMonitor integrations and address connector, schema, and data quality issues. Data Modeling, Governance & Quality\nDevelop and maintain effective data models aligned with reporting, analytics, and business requirements.\nSupport semantic layer design and ensure data structures are optimized for downstream analytics.\nImplement data quality monitoring, validation, lineage, and access controls.\nIdentify data quality risks and recommend practical improvements.\nEnsure solutions follow appropriate standards for security, governance, testing, and documentation. Monitoring & Continuous Improvement\nMonitor pipeline performance, connector health, schema changes, and data quality.\nTroubleshoot and resolve data integration, transformation, and performance issues.\nIdentify opportunities for automation, standardization, and process improvement.\nDevelop and maintain reusable pipeline patterns, delivery templates, documentation, and engineering playbooks.\nSupport ongoing enhancements, new source integrations, platform expansion, and optimization initiatives. Collaboration & Client Engagement\nCollaborate with Analytics Engineers to ensure data models and pipelines support business and reporting requirements.\nWork closely with project managers, solution architects, engineers, and other technical teams.\nParticipate in discovery sessions, workshops, solution design discussions, and client-facing engagements.\nTranslate technical concepts, risks, tradeoffs, and recommendations into clear business terms.\nProactively communicate project risks, dependencies, assumptions, and potential challenges.\nContribute to mentoring, knowledge sharing, and continuous improvement across the engineering team.\nRequirements\n3+ years of experience building and supporting production data pipelines using ETL/ELT processes in cloud or modern data environments.\nStrong experience with modern data platforms and data engineering technologies.\nProficiency in SQL, Python, and PySpark or equivalent data processing technologies.\nStrong data modeling experience, including dimensional modeling and modern data architecture patterns.\nExperience building, maintaining, and troubleshooting ETL/ELT and reverse ETL processes.\nExperience with Microsoft Fabric, Azure Data Factory, or an equivalent modern cloud data platform.\nExperience with data integration platforms such as Fivetran, CData, native connectors, APIs, or equivalent technologies.\nUnderstanding of data governance fundamentals, including data quality, lineage, access controls, and monitoring.\nStrong problem-solving and analytical skills.\nStrong written and verbal communication skills.\nAbility to collaborate effectively with engineering, analytics, BI, project management, and business teams.\nStrong documentation and organizational skills.\nAbility to work independently while contributing effectively within a collaborative team environment.\nComfortable receiving and providing constructive feedback.\nAbility to understand client objectives and translate business requirements into practical technical solutions. Nice to Have\nHands-on experience with Microsoft Fabric.\nConsulting or client-facing experience.\nExperience working in collaborative pod or cross-functional team structures.\nExperience with Spark, Lakehouse architectures, and medallion architecture.\nExperience with Fivetran, CData, or similar integration platforms.\nExperience supporting analytics, AI, or business intelligence initiatives.\n• Experience developing reusable engineering standards, playbooks, and delivery assets.","description_format":"text","description_chars":4977,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Business Process Outsourcing (BPO)","IT Staffing & Staff Augmentation"],"lifecycle":[{"event":"open","at":"2026-09-24T17:28:27Z"}],"liveness":{"score":47,"band":"ok","label":"Likely open","p_open":1,"p_active":0.496,"p_room":0.945,"age_days":9,"expected_fill_days":23,"reasons":["conf:4","win:mid"],"computed_at":"2026-09-25T05:45:01Z"},"pay":null,"html_url":"https://alion.io/job/scalable-os-data-engineer","json_url":"https://alion.io/job/scalable-os-data-engineer.json","meta":{"generated_at":"2026-09-26T02:36:23Z","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":2719,"day_limit":5000,"remaining_today":2281,"minute_limit":60,"resets_at":"2026-09-27T00:00:00Z"}}}