{"id":517898,"url":"https://alion.io/job/prudential-singapore-senior-manager-data-platform-engineering","title":"Senior Manager, Data Platform Engineering","company":{"id":232568,"name":"Prudential Singapore","domain":"prudential.com.sg","url":"https://alion.io/company/prudential-2","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":84,"ghost_share":0,"stale_share":0.81,"repost_share":0,"time_to_fill_p50_days":47,"computed_at":"2026-10-01T05:45:00Z"}},"role":"DevOps","role_family":"DevOps","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Kuala Lumpur, Malaysia"],"countries":["MY"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19500,"max_usd":47000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":602},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache Kafka","optional":false},{"name":"Azure","optional":false},{"name":"Azure Cosmos DB","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"CI/CD","optional":false},{"name":"Collibra","optional":false},{"name":"Databricks","optional":false},{"name":"DataStage","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"Feature Store","optional":false},{"name":"Fivetran","optional":false},{"name":"GraphQL","optional":false},{"name":"Informatica","optional":false},{"name":"Master Data Management","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"Replicate","optional":false},{"name":"Salesforce Data Cloud","optional":false},{"name":"Scala","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Apex","optional":true}],"status":"live","first_seen_at":"2026-07-28T00:00:00Z","employer_posted_date":"2026-07-28","last_verified_at":"2026-10-01T00:25:09Z","board_verified":true,"closed_at":null,"days_open":65,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":65},"description":"Prudential’s purpose is to be partners for every life and protectors for every future. Our purpose encourages everything we do by creating a culture in which diversity is celebrated and inclusion assured, for our people, customers, and partners. We provide a platform for our people to do their best work and make an impact to the business, and we support our people’s career ambitions. We pledge to make Prudential a place where you can Connect, Grow, and Succeed.\nSenior technical and people leader responsible for driving the end-to-end delivery of the Data Lake platform, Enterprise Data Model, data ingestion, and curated data services that power data & analytics, AI, regulatory, and business reporting needs. The role leads a multi-disciplinary team of data engineers and contingent specialists, while partnering closely with Data Governance, Enterprise Architecture, AI Engineering, Business Units, Group Technology, and external vendors to deliver trusted, governed, and scalable data assets.Key Responsibilities :\n1. Team Leadership\nLead, coach, and develop a team of data engineers, platform engineers, and contingent workers across onshore and offshore models, including direct line management of permanent staff and contractors.\nProvide day-to-day technical guidance, code/design reviews, and architectural direction to the team on Azure Databricks, ADF, Cosmos DB, MongoDB, SQL, Python/Scala, Qlik Replicate, Fivetran, Enterprise Kafka, DataStage, Informatica and ingestion frameworks.\nDrive performance management, career development planning, and succession planning for the team.\nBuild a high-performing culture of accountability, quality, and continuous learning; identify upskilling/cross-training needs and nominate talent for recognition.\nSupport hiring, interviewing, and onboarding of new team members and vendor resources.\n2. Data Platform Engineering & Architecture Delivery\nOwn the roadmap, design, build, and operations of the Data Lake platform, including raw, structured, EDM, and curated/serving layers on Azure Databricks and Azure Data Stack.\nDrive the data uplift programme and convergence/decommissioning initiatives (e.g., convergence, legacy data store remediation).\nLead the design of scalable, reusable ingestion patterns (batch and near real-time) from core systems such as policy admin system, claims, marketing, and partner systems.\nGovern adoption of the group data model alignment (including SCD2 patterns, incremental processing, timestamp metadata) in line with Group/Regional standards.\nChampion engineering best practices: CI/CD, modular pipeline design, parameterised/config-driven frameworks, monitoring, and observability.\n3. Data Governance, Quality & Compliance Support\nPartner with the Data Governance team to operationalise the Group Data Governance Operating Minimum Standards (data dictionary, CDE prioritisation, data quality, lineage, master data, data extraction).\nProvide technical stewardship for business and technical data lineage and support development/implementation of Data Quality rules and remediation.\nAct as the technical approver/gatekeeper for data lake access provisioning, PII access, schema-level entitlements, and data sharing (internal and external/RFP/vendor scenarios).\nSupport responses to regulators (e.g., Bank Negara Malaysia / BNM) on data governance, manual submissions to core systems, PIA, and legacy remediation plans.\n4. Business Continuity, Risk & Operational Resilience\nServe as the department's Business Continuity Coordinator (BCC) for Data Management, owning the BCP, call tree, alternate-site/WFH arrangements, and annual BCP exercises.\nEnsure DR readiness, IT DR test participation, and recovery procedures for critical data platforms and pipelines.\nManage incidents, root cause analysis, and post-mortem reviews for data platform and pipeline disruptions.\n6. Vendor, Sourcing & Cost Management\nLead RFP/RFI evaluations, technical scoring, vendor workshops, and engagement decisions for data-related solutions.\nManage delivery partners and contingent workforce - including scope, SLAs, performance, and commercial governance.\nDrive resource sourcing for specialised roles (Data Engineer, Cosmos DBA, Data Modeler, Solution Architect) including JD preparation and interview leadership.\n7. Documentation, Standards & Continuous Improvement\nEnsure functional, technical, operational, and architecture documentation is maintained for all supported platforms and pipelines.\nEmbed security guidelines for confidential / PII / Restricted-Sensitive data in line with Group Security and Group Data Policy.\nIdentify automation and optimisation opportunities to improve cost, performance, and time-to-insight.\nJob Requirements :\nEducation & Experience\nBachelor's Degree (Master's preferred) in Information Technology, Computer Science, Data/Information Engineering, or related discipline.\n10+ years of progressive experience in data engineering / data platform / data management roles, with at least 4 years in a people leadership capacity managing teams of 5+ engineers.\nProven track record of delivering enterprise-scale data platforms (data lake / lakehouse / EDW) end-to-end in a regulated industry - insurance, banking, or financial services strongly preferred.\nTechnical Expertise\nDeep hands-on expertise in Azure Data Stack: Azure Databricks, Azure Data Factory (ADF), Azure Data Lake, Unity Catalog, Cosmos DB, and Power BI.\nStrong programming proficiency in Python, Scala, and SQL; familiarity with Spark optimisation and Delta Lake.\nSolid understanding of data architecture, dimensional modelling, EDM, MDM (e.g., PruMDM/Reltio), SCD2, data lake/lakehouse, data lineage, reconciliation, and CI/CD for data.\nWorking knowledge of Data Governance & Data Quality tooling - Collibra and/or Informatica (CDGC, IDQ, EDC) is a strong advantage.\nExposure to AI/ML data enablement (MLOps, feature stores, model-ready datasets) and integration with Salesforce Data Cloud is an added advantage.\nFamiliarity with API/streaming integration patterns (Kafka, Event Hub, GraphQL, MongoDB connectors).\nDomain & Regulatory Knowledge\nStrong understanding of the insurance business domain (agency, customer, policy, claims, health, billing, product) and associated data flows.\nAwareness of BNM, PDPA, and Group regulatory expectations on data governance, privacy, and outsourcing.\nLeadership & Soft Skills\nDemonstrated ability to lead, mentor, and grow technical teams, including managing performance, succession, and capability building.\nExcellent stakeholder management - comfortable presenting to and influencing C-level audiences and Group/Regional counterparts.\nStrong executive communication skills: able to translate complex technical topics into business-impact language and concise executive briefs.\nHighly proactive, self-motivated, and accountable; able to cope with challenging delivery timelines and concurrent priorities.\nStrong analytical, problem-solving, and decision-making skills with sound commercial judgement.\nExcellent interpersonal and written/oral communication in English.\nPrudential is an equal opportunity employer. We provide equality of opportunity of benefits for all who apply and who perform work for our organisation irrespective of sex, race, age, ethnic origin, educational, social and cultural background, marital status, pregnancy and maternity, religion or belief, disability or part-time / fixed-term work, or any other status protected by applicable law. We encourage the same standards from our recruitment and third-party suppliers taking into account the context of grade, job and location. 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