{"id":1441202,"url":"https://alion.io/job/combuilder-data-engineer","title":"Data Engineer","company":{"id":236844,"name":"Combuilder","domain":"combuilder.com.sg","url":"https://alion.io/company/combuilder","size_band":"1-10","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"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":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":4500,"max":6000,"currency":"SGD","period":"month","gross":true,"usd_annual":56328},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Flink","optional":true}],"status":"live","first_seen_at":"2026-09-29T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-09-29T00:00:00Z","board_verified":false,"closed_at":null,"days_open":7,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":7},"description":"We are looking for a skilled Data Engineer with hands-on experience in workflow orchestration, data integration, and cloud-based big data platforms. The role focuses on migrating and re-platforming existing integration workflows, building reliable ETL pipelines, and contributing to Lakehouse-style data architectures that support both batch and streaming workloads.\nThe ideal candidate has practical experience working with integration platforms, structured data ingestion, and distributed data processing frameworks, and is comfortable operating in non-trivial enterprise environments with multiple applications, data volumes, and operational constraints.\nKey Responsibilities\nIntegration & Workflow Engineering\nAnalyse and re-platform existing workflow-based integrations from one platform to another while maintaining functional parity.\nImplement orchestration logic including triggers, conditional routing, retries, exception handling, and state management.\nDesign and implement automation patterns to handle platform limitations (e.g., looping, batching, pagination, throttling).\nEnsure workflows are idempotent, fault tolerant.\nSupport environment-based deployments (DEV / UAT / PROD) with configuration-driven designs.\nData Engineering & ETL\nDesign and build ETL pipelines for ingesting flat files (e.g., CSV) into relational databases.\nHandle schema validation, basic schema evolution, data quality checks, and error reconciliation.\nOptimize data ingestion for performance, scalability, and reliability.\nCollaborate with application teams to understand upstream and downstream data dependencies.\nCloud & Big Data Platform Contributions\nBuild and maintain data pipelines on cloud-based big data platforms using distributed processing frameworks.\nContribute to Lakehouse-style data storage that supports both batch and streaming data.\nWork with modern table formats that support incremental processing, versioning, and historical queries.\nSupport use cases such as append-heavy datasets, high-write event data, and analytical queries.\nOperations, Quality & Observability\nImplement logging, monitoring, and alerting for workflows and data pipelines.\nSupport operational readiness including runbooks, deployment procedures, and rollback strategies.\nParticipate in root-cause analysis and continuous improvement of data pipelines.\nEnsure adherence to data governance, security, and compliance standards.\nRequired Skills & Experience\nTechnical Skills\nStrong experience with workflow orchestration / integration platforms.\nSolid understanding of ETL concepts and hands-on experience with file-based ingestion.\nProficient in SQL and working knowledge of relational databases (e.g., SQL Server or equivalent).\nExperience with distributed data processing frameworks (e.g., Spark).\nFamiliarity with streaming and batch data processing concepts.\nPractical experience with cloud platforms and managed data services.\nUnderstanding of CI/CD principles for data and integration workflows.\nProgramming & Automation\nExperience with scripting or programming languages commonly used in data engineering (e.g., Python).\nAbility to build reusable utilities for batching, retries, pagination, and error handling.\nExperience with REST APIs and structured data formats (JSON, CSV).\nDesired (Good to Have)\nExposure to modern Lakehouse table formats supporting incremental processing and time travel.\nExperience with stream processing engines (e.g., Flink or Spark Streaming).\nFamiliarity with query engines used for analytical access.\nExperience working in regulated environments (banking, financial services, or manufacturing).\nKnowledge of data observability and quality frameworks.\nExperience Level\n3–5 years of relevant experience in data engineering, integration engineering, or similar roles.\nProven ability to work independently on moderately complex problems while collaborating within a larger team.\nSoft Skills & Competencies\nStrong analytical and problem-solving skills.\nAbility to understand existing systems and translate requirements into working solutions.\nClear communication with technical and non-technical stakeholders.\nDetail-oriented with a focus on reliability and maintainability.\nComfortable working across multiple teams and applications.\nSkills\nUAT, Stream Processing, engineering queries, Regulatory Compliance, ETL Tools, Data Quality Assurance, Automation Design, IP Routing, Fault Tolerance, Platform Migration, Banking, Experience Design, Collaborate With Internal Team, development of soft skills, Financial Services, Exception 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