{"id":1218005,"url":"https://alion.io/job/maya-senior-analytics-engineer-2","title":"Senior Analytics 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":"Analytics","role_family":"Analytics","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":14500,"max_usd":33000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":3468},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-08-30T16:00:00Z","employer_posted_date":"2026-08-30","last_verified_at":"2026-10-29T04:03:35Z","board_verified":true,"closed_at":null,"days_open":60,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":60},"description":"As an Analytics Engineer, you will be responsible for designing, developing, and maintaining scalable data products, feature stores, data models, and transformation frameworks that enable Data Scientists to efficiently build, deploy, and operationalize machine learning solutions.\nYou will serve as the bridge between Data Engineering and Data Science, ensuring that high-quality, trusted, and well-modeled data is readily available for analytics, experimentation, and machine learning use cases. The ideal candidate has strong experience in dbt, SQL, data modeling, Databricks, cloud platforms, and feature engineering pipelines.\nWHAT YOU WILL DO:\nData Modeling & Transformation\nPartner closely with Data Scientists to design, develop, and maintain datasets that support statistical analysis, machine learning models, experimentation, and business insights. \nDesign, build, and optimize scalable data models using dbt (Data Build Tool) following industry best practices for modularity, testing, documentation, and maintainability. \nDevelop and manage transformation pipelines that convert raw data into trusted, analytics-ready datasets. \nEstablish and maintain data lineage, governance, and documentation across analytical data assets. \nFeature Store & ML Data Platform\nDesign, develop, and maintain feature stores and accompanying feature pipelines used for model training, feature serving, and batch inference. \nImplement reusable feature engineering frameworks that accelerate machine learning development and improve feature consistency. \nCollaborate with Data Scientists and Machine Learning Engineers to ensure feature reusability and production readiness. \nSupport the design and maintenance of real-time and batch feature pipelines. \nData Quality, Governance & Reliability\nImplement automated data validation, monitoring, and testing frameworks to ensure data quality and reliability. \nEstablish data quality checks, SLAs, observability metrics, and monitoring processes across critical datasets and pipelines. \nPartner closely with Data Engineering and Data Governance teams to improve data maturity, accessibility, and trustworthiness. \nServe as a subject matter expert across assigned business and data domains. \nPlatform Engineering & Best Practices\nBuild CI/CD pipelines that accelerate deployment and proactively detect issues before production release. \nCreate and maintain reusable software packages, tools, and frameworks that improve Data Science productivity. \nEstablish best practices for data modeling, transformation, testing, deployment, and architecture. \nConduct code reviews and architectural reviews to ensure adherence to engineering standards. \nCommunicate technical architectures, trade-offs, and solutions to both technical and non-technical stakeholders. \nMaintain architecture diagrams, technical documentation, and platform standards. \nWHAT WE'RE LOOKING FOR:\nBachelor's degree in Computer Science, Information Technology, Statistics, Engineering, Mathematics, or a related quantitative discipline. \nAt least 4+ years of experience in Analytics Engineering, Data Engineering, or related fields. \nAt least 2+ years of hands-on experience with dbt (Data Build Tool) in a production environment. \nStrong expertise in SQL, data modeling, and ETL/ELT development. \nExperience with Databricks, Apache Spark, or modern Lakehouse platforms. \nExperience working with cloud technologies such as AWS, Azure, or GCP. \nExperience 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