{"id":2379160,"url":"https://alion.io/job/pmconsulting-databricks-data-engineer","title":"Databricks Data Engineer","company":{"id":4421389,"name":"PM Consulting","domain":"pmconsulting.ph","url":"https://alion.io/company/pmconsulting-2","size_band":"1001-5000","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Manatal","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cebu City, Philippines"],"countries":["PH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Fivetran","optional":false},{"name":"GCP","optional":false},{"name":"Hadoop","optional":false},{"name":"Informatica","optional":false},{"name":"Looker","optional":false},{"name":"Machine Learning","optional":false},{"name":"Matillion","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Talend","optional":false}],"status":"live","first_seen_at":"2026-10-12T18:59:17Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-17T17:57:41Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"Role Overview\nA leading global technology organization is looking for a Databricks Data Engineer to design, build, and optimize modern data platforms in a cloud environment. This role focuses on developing scalable data pipelines, ensuring data quality, and enabling advanced analytics and AI use cases.\nThe ideal candidate brings strong experience in big data processing, cloud technologies, and the Databricks ecosystem, with a focus on building efficient, secure, and production-ready data solutions.\nKey Responsibilities\nData Engineering & Pipeline Development\nDesign, develop, and maintain scalable ETL/ELT pipelines for data ingestion, transformation, and processing.\nBuild high-performance data workflows using the Databricks ecosystem, including Delta Lake and structured streaming capabilities.\nImplement reusable data transformation and validation logic for consistency across pipelines.\nEnsure data quality, integrity, and reliability across all data processing stages.\nCloud & Platform Engineering\nDevelop and deploy data solutions on at least one major cloud platform:\nAmazon Web Services\nGoogle Cloud Platform\nMicrosoft Azure\nConfigure secure data environments, including access controls, auditing, and data governance.\nEnable cross-platform data access and sharing using modern data architecture patterns.\nAdvanced Data Processing & Optimization\nBuild efficient data transformation pipelines using PySpark and Python.\nOptimize performance of data jobs through tuning, resource management, and cost optimization techniques.\nImplement data layering strategies (e.g., Bronze/Silver/Gold architecture) for structured data processing.\nData Integration & Analytics Enablement\nIntegrate data platforms with BI and analytics tools such as:\nMicrosoft Power BI\nTableau\nLooker\nDevelop dashboards and reporting solutions to monitor KPIs, pipeline health, and operational metrics.\nSupport data sharing initiatives and enable collaboration across teams and domains.\nAI/ML & Data Innovation\nPrepare datasets for advanced analytics and AI use cases, including support for machine learning workflows.\nWork with modern tools for experimentation, feature management, and model lifecycle support.\nExplore new technologies and approaches to enhance data engineering capabilities.\nDevOps & Deployment\nPackage and deploy data pipelines using CI/CD practices and version control systems.\nCollaborate with engineering teams to ensure reliable and repeatable deployments.\nQualifications and Experience\nAt least 2 years of experience in data engineering or related roles.\nStrong hands-on experience with Python and PySpark for data processing.\nProven experience building data pipelines in cloud-based environments.\nSolid understanding of data modeling, data warehousing, and ETL/ELT concepts.\nExperience working with large-scale, distributed data systems.\nTechnical Skills\nExpertise in:\nApache Spark / PySpark\nDelta Lake and modern data lakehouse architecture\nSQL and data transformation logic\nFamiliarity with:\nData orchestration tools (e.g., Apache Airflow)\nBig data technologies (e.g., Hadoop, Kafka, Hive)\nETL tools such as Informatica, Talend, Matillion, or Fivetran\nData build tools (e.g., dbt)\nPreferred Qualifications\nDatabricks or cloud certifications\nExposure to machine learning concepts and frameworks\nExperience with DevOps practices and CI/CD pipelines\nKnowledge of real-time streaming and serverless architectures\nSkills and Competencies\nStrong analytical and problem-solving skills\nAttention to detail and commitment to data quality\nAbility to work independently and in collaborative environments\nEffective communication skills for both technical and non-technical stakeholders\nAdaptability in a fast-paced, technology-driven environment\nWhy Consider This Role\nOpportunity to work on modern data platforms and cloud technologies\nExposure to AI/ML and advanced analytics use cases\nHands-on experience with Databricks and lakehouse architecture\nCollaborative environment focused on innovation and continuous 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