{"id":1249179,"url":"https://alion.io/job/changi-data-platform-specialist-quality-management","title":"Data Platform Specialist (Quality Management)","company":{"id":450262,"name":"Changi Airport Group","domain":"changiairport.com","url":"https://alion.io/company/changiairport-2","size_band":"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":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":36000,"max_usd":73000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":13},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Google BigQuery","optional":false},{"name":"IAM","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false}],"status":"live","first_seen_at":"2026-09-01T00:00:00Z","employer_posted_date":"2026-09-01","last_verified_at":"2026-10-16T00:46:51Z","board_verified":true,"closed_at":null,"days_open":45,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":45},"description":"The Data Quality Engineer / Platform Specialist will play a hands-on technical role in ensuring that data within the Enterprise Data Platform (EDP) is accurate, reliable, discoverable, and fit for business use. Focusing on our GCP BigQuery ecosystem, you will design, implement, and automate data quality checks, manage platform health, and troubleshoot data pipelines. While experience is valued, we are highly focused on finding someone with strong SQL capabilities, an analytical mindset, and a deep understanding of modern, cloud-native data platforms.\nKey Responsibilities\n1. Technical Data Quality & Automation\nDevelop, deploy, and maintain automated data quality rules and tests natively within GCP (e.g., using Dataform or BigQuery stored procedures) for critical data assets.\nDesign and build monitoring, alerting, and scorecard dashboards to proactively detect data anomalies, schema drifts, and quality deterioration before they impact downstream reporting.\nPerform deep-dive root cause analysis using advanced SQL to trace data incidents back to their source, coordinating technical fixes with data engineers and system owners.\n2. GCP Platform Operations & Management\nAssist in managing the GCP BigQuery data platform environment, ensuring efficient query performance, cost management, and reliable data transformation pipelines.\nImplement platform governance guardrails for data onboarding, transformation, and exception handling. \nUtilise native GCP tools (such as Cloud Composer, Knowledge Catalogue, or Cloud Functions) to orchestrate quality checks and remediate data pipeline failures.\nWhile GCP-native skills are the priority, experience with comparable enterprise data quality tools or data platforms like Databricks, AWS, Azure, IDMC will be considered as well.\n3. Data Quality by Design (DQxD) Implementation\nAct as the technical execution arm for CAG’s Data Quality by Design framework across new digital and analytics initiatives. \nPartner with project teams to review data models and integrations, ensuring data quality tests are coded and implemented in the data pipeline before data is consumed.\n4. Technical Metadata & Cataloging\nUtilise tools like GCP Knowledge Catalog to maintain technical metadata, ensuring the Data Catalogue, Data Dictionary, and Data Lineage remain highly accurate and discoverable. \nAutomate the extraction and updating of metadata from source systems into the centralised catalog.\nRequirements\n\nEducation & Experience\nDegree in Computer Science, Information Systems, Data Analytics, Engineering, Statistics, or a related highly quantitative discipline. \n0-3 years of experience.\nCandidates with strong academic projects, relevant internships, or a demonstrable portfolio in data engineering, data quality, or cloud platforms are highly encouraged to apply.\nCore Technical Skillsets\nAdvanced SQL & Query Optimisation\nComplex Querying: Strong proficiency in writing advanced SQL (including Common Table Expressions (CTEs), window functions, complex joins, and handling JSON/nested data structures). \nAnalytical Troubleshooting: Ability to use SQL for deep-dive root cause analysis and data profiling to isolate pipeline failures and data anomalies.GCP & BigQuery Native Ecosystem\n\nPlatform Architecture: Hands-on experience or strong conceptual understanding of Google BigQuery architecture and navigating enterprise-scale data environments. \nNative Governance Tools: Familiarity with GCP-native metadata management and governance tools, specifically Knowledge Catalog, for automating data quality checks, data cataloguing, and mapping data lineage. \nCloud Infrastructure: Basic understanding of the broader GCP ecosystem, including Cloud Storage (GCS), Identity and Access Management (IAM) for security guardrails, and Cloud Functions/PubSub for event-driven alerting.\nData Quality & Observability Engineering\nQuality Frameworks: Strong technical grasp of core data quality management dimensions (accuracy, completeness, consistency, validity, and timeliness) and how to translate them into executable code. \nMonitoring & Remediation: Experience designing automated monitoring routines, anomaly detection, and issue remediation workflows.\nData Pipelines, Modelling & Orchestration (ETL/ELT)\nData Modelling: Solid understanding of modern data warehousing concepts, dimensional data modelling, and scalable ELT architectures.\nTransformation & Orchestration: Familiarity with SQL-first transformation frameworks like Dataform, and an understanding of orchestration concepts using tools like Google Cloud Composer.\nDevOps for Data (CI/CD): Understanding of version control (Git) and the basics of deploying data pipelines and quality tests through CI/CD pipelines.\nProgramming & Automation\nPython Proficiency: Intermediate skills in Python for programmatic data manipulation, scripting, and automating operational tasks.\nBI, Analytics & Visualisation\nDashboard Development: Familiarity with BI / analytics platform concepts to support downstream users. \nQuality Scorecards: Ability to build operational scorecards and data observability dashboards using visualisation tools such as Tableau, or Power BI to report on data health.\nCore Competencies\nStrong analytical and problem-solving skills with a highly structured, code-first approach to root cause analysis. \nHigh attention to technical detail and the ability to translate business requirements into hard-coded data quality rules and monitoring mechanisms. \nClear communication skills, specifically the ability to explain complex pipeline failures or data quality risks to non-technical stakeholders. \nThis is a 3-yr contract role.","description_format":"text","description_chars":5634,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Transportation & 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