{"id":1218001,"url":"https://alion.io/job/maya-senior-data-engineer","title":"Senior Data Engineer","company":{"id":50036,"name":"Maya","domain":"maya.ph","url":"https://alion.io/company/maya-ph","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":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mandaluyong, Philippines"],"countries":["PH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":18000,"max_usd":44000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1254},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon EC2","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Delta Lake","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"CI/CD","optional":true},{"name":"Platform Engineering","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-08-31T16:00:00Z","employer_posted_date":"2026-08-31","last_verified_at":"2026-09-25T11:21:55Z","board_verified":false,"closed_at":null,"days_open":27,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":27},"description":"CORE PROFILE \nThe Data Engineer is responsible for the creation, maintenance, and continuous improvement of data pipelines. This includes implementing best practices in data management such as data cleaning, validation, and transformation, ensuring that data is structured into high-quality datasets that can be efficiently consumed by downstream teams.\nThis role works closely with software engineers, data analysts, data scientists, and data governance teams to understand data behavior within specific domains, clarify business and technical requirements, and translate data use cases into efficient, reliable, and maintainable data pipelines.\nThe Data Engineer plays a critical role in enabling advanced analytics and AI use cases by ensuring that data pipelines, datasets, and platforms are reliable, scalable, and suitable for machine learning and real-time decision systems.\nWithin Data Engineering, whether in Central DE or Distributed DE, this role applies and continuously improves best practices in data management, data architecture, and DataOps. The Data Engineer is essential in ensuring data availability, reliability, and usability, enabling teams across the organization to generate insights and deliver value through data.\nNATURE OF WORK\nThe Data Engineer works on existing data pipelines, including the development of data models and data management across data warehouses, data lakes (Delta Lake), and lakehouse architectures. The role collaborates with upstream teams (e.g., Mesh teams) that provide data into the platform, as well as downstream teams that consume and operationalize data. The role understands existing technology choices, and adopts new technology & practices that consistently comply with Data Engineering standards.\nCentral DE\nIndependently designs and builds new data pipelines within existing architectures\nOwns ingestion and transformation logic end-to-end, ensuring solutions meet both functional and long-term requirements\nHandles both new and legacy pipelines with minimal guidance\nImproves pipeline reliability, performance, and scalability\nLeads projects, not just development tasks\nDesigns solutions spanning multiple pipelines or domains, including those supporting analytical and advanced data use cases\nNavigates technical dependencies across teams and systems\nDrives improvements in data integrity, timeliness, and quality within assigned domains\nMentors junior engineers and provides guidance on implementation, best practices, and technical decision-making\nAI & Advanced Analytics Enablement\nDesigns and maintains data pipelines that support advanced analytics and AI use cases\nEnsures data quality, consistency, and availability for analytical and data-driven applications\nCollaborates with downstream teams to align data models and pipelines with evolving analytical requirements\nAI-Assisted Development Practice\nUses AI-assisted tools to improve productivity and efficiency in development\nRemains fully accountable for the technical correctness and quality of all outputs\nReviews, validates, and challenges AI-generated code, recommendations, and design proposals\nEnsures all solutions align with Data Engineering standards, security requirements, maintainability, and long-term reliability\nDISPLAYED SKILL MASTERY\nCommon Skills\nProficiency in Shell scripting (e.g., bash, zsh)\nStrong proficiency in data manipulation using SQL and experience with structured and semi-structured data (e.g., JSON, NoSQL)\nSolid working knowledge of cloud platforms (e.g., AWS services such as S3, EC2, Glue, Lambda, Athena, etc.) or equivalent\nProficiency in Apache Spark using SQL and/or Python for large-scale data processing\nStrong understanding of data storage and processing architectures, including Data Warehouse, Data Lake, Delta Lake, and Lakehouse\nAbility to collaborate effectively with cross-functional teams and contribute to a culture of technical excellence and accountability\nFamiliarity with AI-assisted development tools and ability to critically validate generated outputs\nCentral DE\nAdvanced expertise in designing and implementing scalable data ingestion pipelines across batch, CDC, and streaming patterns\nStrong design skills across distributed data processing systems, including batch, streaming, and hybrid architectures\nDeep understanding of data modeling, pipeline performance optimization, and cost management in cloud environments\nStrong troubleshooting skills across distributed systems, with the ability to identify and resolve complex data pipeline and infrastructure issues\nGood understanding of data pipeline reliability practices, including monitoring, observability, failure handling, and recovery strategies\nAbility to lead technical design discussions, review solutions, and guide implementation across teams\nProven ability to mentor engineers and enforce engineering standards and best practices\nREQUIRED QUALIFICATIONS\nEducation\nBachelor’s degree in Computer Science, Information Technology, Engineering, or a related field\nMaster’s degree is a plus but not required\nExperience\n4+ years of experience in data engineering, data platform engineering, or related roles\nProven experience designing and building scalable data ingestion and data processing pipelines in cloud-based environments\nStrong hands-on experience with distributed data processing frameworks (e.g., Apache Spark) and cloud services (e.g., AWS or equivalent)\nExperience working with both batch and near real-time or streaming data ingestion pipelines\nExperience managing data pipelines across data lakes, data warehouses, or lakehouse architectures\nDemonstrated ability to lead end-to-end delivery of data pipeline and infrastructure solutions\nExperience troubleshooting and resolving complex data pipeline or production issues across distributed systems\nExperience collaborating with upstream data providers (e.g., source systems, platform teams) and downstream consumers (e.g., analytics or data science teams\nTechnical Expertise\nAdvanced proficiency in SQL and strong programming skills in Python or equivalent\nStrong understanding of data ingestion patterns (batch, CDC, streaming)\nSolid understanding of distributed data architectures (Data Lake, Delta Lake, Lakehouse)\nExperience with performance optimization and cost management in cloud-based data platforms\nExperience with CI/CD practices and infrastructure-as-code tools (e.g., Terraform) is highly preferred\nAI & Advanced Analytics Awareness\nUnderstanding of how data pipelines support downstream advanced analytics and AI use cases\nAbility to design pipelines that provide consistent, reliable, and timely data for analytical workloads\nFamiliarity with data quality, completeness, and reliability requirements for downstream model consumption\nExperience using AI-assisted development tools and the ability to critically validate generated outputs\nBehavioral & Leadership Competencies\nAbility to lead technical discussions and drive data pipeline design decisions across teams\nStrong problem-solving skills in distributed and data-intensive systems\nEffective communication skills across technical and non-technical stakeholders\nProven ability to mentor engineers and review technical 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open","p_open":1,"p_active":0.753,"p_room":0.75,"age_days":26,"expected_fill_days":30,"reasons":["conf:42","velocity","win:late"],"computed_at":"2026-09-27T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/maya-senior-data-engineer","json_url":"https://alion.io/job/maya-senior-data-engineer.json","meta":{"generated_at":"2026-09-28T03:21:25Z","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":2097,"day_limit":5000,"remaining_today":2903,"minute_limit":60,"resets_at":"2026-09-29T00:00:00Z"}}}