Top Data Analyst Jobs in Manila

Collect, verify, and analyze governance data for assigned markets; respond to internal/client queries; follow data methodology and quality standards; meet turnaround goals; escalate and root-cause data issues; update performance monitoring documents; participate in projects and committees; maintain required paper and electronic files per retention policies.

Performs laboratory sample registration, data entry, record cleanup, report preparation, billing support, CRM maintenance, quotations, and data-quality verification. Uses LIMS and database systems to manage accurate laboratory reports, meets turnaround deadlines, responds to technical queries, supports operational improvements, and follows SGS quality, integrity, QHSE, and HR procedures. The role is hybrid but may require onsite work for two to six months.

Own internal reporting and analytics by building Power BI dashboards, writing SQL, developing data models, integrating data sources, and delivering business insights. Improve data quality, governance, automation, and refresh reliability while exploring AI tools such as Claude and Microsoft Copilot. Partner with stakeholders, validate findings, and present recommendations to technical and non-technical audiences.

Manage customer data processes, governance, privacy compliance, quality maintenance, segmentation, and marketing list extraction. Partner with marketing, analytics, data, show, and global privacy teams to improve data flows across Salesforce, registration systems, and campaign platforms. Create reports and queries, validate and cleanse records, reduce duplication, support personalization initiatives, and provide stakeholders with data-quality insights and resolution feedback.

Manages customer account, security, pricing, and reference data across multiple systems and teams. Monitors data quality, identifies process improvements, supports client service levels, and contributes to project delivery. Provides team guidance, supervises junior staff, evaluates performance, identifies training needs, and acts as manager backup. Ensures risk management, regulatory compliance, policy adherence, and transparent escalation of control issues within a global financial services environment.

Analyzes and visualizes post-market surveillance and quality-system data to support complaint handling, investigations, CAPA, regulatory reporting, audits, and inspection readiness. The role prepares reports and regulatory content, applies statistical techniques, identifies product quality trends, and recommends process improvements while partnering with Post-Market Quality and cross-functional stakeholders.

Own and maintain the credit card NPV and unit economics model. Build production-ready Python and SQL logic, create datasets covering cohorts, cashflows, losses, funding, fees, and revenue, automate scenario analyses, validate model outputs against portfolio performance, investigate variances, and document implementation-ready specifications.

Remediate client and security reference data, perform trend and root-cause analysis, coordinate globally to resolve data-quality issues, support internal stakeholders, manage ad hoc projects, provide management metrics, and ensure compliance with applicable policies and regulations.

Leads sales and orders reporting, incentive calculations, performance analytics, data quality validation, and process improvements for Vertiv Asia. Analyzes pipeline, orders, backlog, win rates, and business results to support sales leaders and strategic decisions. Coordinates with sales, finance, HR, IT, and regional stakeholders; manages commission-related inquiries, improves reporting through Power BI and automation, supports business continuity, and mentors junior team members.

Analyzes complex datasets and delivers actionable insights through Power BI reports and dashboards. Maintains Azure Data Lake integrity, performs data cleansing and transformation, supports ETL and governance initiatives, and collaborates with IT and business stakeholders on data requirements and accuracy. Provides user training and support while staying current with data management practices. Requires strong SQL, Azure Databricks, Python or R, data modeling, warehousing, and data quality expertise.

Consult with stakeholders to define questions, prepare and clean large datasets, run intermediate statistical analyses, build visualizations (Tableau/Power BI), lead small analytics projects, and communicate actionable insights. Uses SQL/Python and basic big-data tools; promotes analytics best practices and leverages approved AI tools to accelerate workflows.

Leads technical data projects supporting cross-border remittances, including data modeling, advanced analytics, automated scripting, reconciliation, and data transformation. Partners with analysts and business stakeholders to deliver reliable self-service data and ensure financial accuracy. Requires Python, SQL, Databricks, strong analytical skills, and financial services experience. Preferred qualifications include AutoRek or ReconArt reconciliation tools and Tableau, Power BI, or Looker expertise.

Design, develop, and maintain ETL workflows with Informatica; support MDM processes; analyze data to surface insights; improve data quality; produce reports and dashboards; collaborate with stakeholders and IT on governance and data solutions.

Perform ETL development and maintenance in Informatica, support MDM processes, analyze datasets for trends and insights, monitor and improve data quality, collaborate with stakeholders and IT, and produce reports and dashboards to inform business decisions.

Monitor and record shipment statuses during a fixed night shift, update and complete shipment records in the CRM via call verification with delivery providers, clear statuses, and escalate issues or incomplete records to the client.

Design, develop, and maintain interactive Tableau dashboards; gather stakeholder requirements; optimize SQL queries and data models; ensure data accuracy; produce reports and provide dashboard support and troubleshooting.

The Business Systems Analyst will update data in Salesforce, manage projects, collaborate across departments, conduct business analysis, and document processes.

The Senior Financial Data Analyst manages market and master data collection and analysis, ensuring compliance with SLAs and operational risk frameworks to support client needs and improve processes.

Analyze complex datasets, design and validate data models and solutions, and ensure high-quality data supports KYC and financial crime prevention applications. The role covers AML, CTF, sanctions, fraud prevention, and regulatory data governance across global teams. Responsibilities include SQL analysis, data-flow and integration work, metadata and source-to-target mapping, incident management, production issue resolution, stakeholder collaboration, and delivery in an international Agile environment.

The Data Analyst (HRIS) will collaborate with stakeholders to define data needs, prepare reports, perform statistical analyses, and ensure data security while delivering insights and assisting with data projects.

Own and analyze marketing channel/program performance end-to-end. Build/maintain GA4 and Salesforce Marketing Cloud dashboards, perform attribution and ROI analysis, translate data into stakeholder-ready recommendations, identify data quality issues, and mentor junior analysts.

Maintain and refresh marketing dashboards, pull and validate campaign performance data, perform QA on data feeds, support ad hoc requests, document report logic, and escalate anomalies to senior analysts.

Lead marketing analytics for US marketing leadership: design scalable measurement frameworks, run advanced attribution and cohort analyses, build dashboards and self-service reporting, own data governance across GA4/Salesforce/Adobe, mentor junior analysts, and present recommendations to senior stakeholders to drive business impact.

As a Databricks Data Analyst, you will support data pipeline validation, monitor ingestion jobs, perform schema checks, and manage documentation.

Own data quality and root-cause analysis for prediction issues: detect anomalies, trace sources, fix processes, automate QA/reporting with Python and SQL, maintain dashboards, and partner with Data Science, Engineering, Product, and Commercial teams to improve prediction performance and customer-facing analytics.