2,806 Data jobs in Singapore

C-AB-002

Key Responsibilities:

· The client wants to create data integration, data marts, and data lakes in accordance with their data architect.

· However, rather than simply following instructions, it will be necessary to consider the current and future styles of data, communicate with multiple members, and perform design work when necessary (in other words, this role will require strong business analysis skills).

· We are looking for personnel who can provide this support.

Key Requirements:

· Bachelor's degree in computer science, Information Systems, Data Science, or a related field. Master's degree preferred.

· 8-10 years of experience in data architecture, data modelling, or data management, preferably in the banking or financial services industry (must have).

· Basic knowledge of data governance (i.e. DAMA-DMBOK).

· Deep expertise in logical and physical data modelling for core banking, risk, compliance, and financial reporting domains and various analytics use cases.

· Proficient in data modelling tools (e.g., Erwin, PowerDesigner, or similar).

· Advanced SQL skills and strong ability to work with large, complex financial datasets.

· Solid understanding of relational and dimensional modelling, data warehousing, and data integration techniques.

· Intermediate understanding of regulatory compliance in the APAC region.

· Strong collaboration, communication, and documentation skills to engage with both business and technical stakeholders.

· Need to Power BI experiences

· People who can communicate and create basic reports

How to Apply:

If interested, please send your resume to or by clicking the "Apply Now" button.

We regret that only short-listed applicants will be notified.

Rachel Ling

Registration No: R21102195

EA Licence No: 21C0761

C-AB-002

Key Responsibilities:

· The client wants to create data integration, data marts, and data lakes in accordance with their data architect.

· However, rather than simply following instructions, it will be necessary to consider the current and future styles of data, communicate with multiple members, and perform design work when necessary (in other words, this role will require strong business analysis skills).

· We are looking for personnel who can provide this support.

Key Requirements:

· Bachelor’s degree in computer science, Information Systems, Data Science, or a related field. Master's degree preferred.

· 8-10 years of experience in data architecture, data modelling, or data management, preferably in the banking or financial services industry (must have).

· Basic knowledge of data governance (i.e. DAMA-DMBOK).

· Deep expertise in logical and physical data modelling for core banking, risk, compliance, and financial reporting domains and various analytics use cases.

· Proficient in data modelling tools (e.g., Erwin, PowerDesigner, or similar).

· Advanced SQL skills and strong ability to work with large, complex financial datasets.

· Solid understanding of relational and dimensional modelling, data warehousing, and data integration techniques.

· Intermediate understanding of regulatory compliance in the APAC region.

· Strong collaboration, communication, and documentation skills to engage with both business and technical stakeholders.

· Need to Power BI experiences

· People who can communicate and create basic reports

How to Apply:

If interested, please send your resume to or by clicking the “Apply Now” button.

We regret that only short-listed applicants will be notified.

Rachel Ling

Registration No: R21102195

EA Licence No: 21C0761

We are looking for a highly experienced Subject Matter Expert (SME) in Data Management (FS) to join our team. The ideal candidate will have 12-15 years of experience in data governance, financial service data solutions, and data engineering. They will also have strong solution and delivery skills, as well as a view on business growth and managing stakeholders.

Responsibilities:

• Serve as a technical advisor and consultant to the Finance team on all aspects of financial data management.
• Develop and implement data governance policies and engineering solutions.
• Design and implement financial service data solutions, including data warehousing, data lakes, and big data analytics platforms, using PySpark, Hadoop, ETL/ELT Tools, AWS/Azure as a hypervisor.
• Work with the data engineering team to develop and maintain data pipelines and infrastructure.
• Collaborate with business stakeholders to understand their data needs and develop solutions that meet those needs.
• Manage and deliver complex data management projects on time and within budget.
• Stay up-to-date on the latest trends and technologies in financial data management.

Qualifications:

• Bachelor's degree in Computer Science, Information Technology, or a related field.
• 12-15 years of experience in financial data management, data governance, and data engineering.
• Strong knowledge of financial service data solutions, including data warehousing, data lakes, and big data analytics platforms.
• Experience with data governance frameworks and best practices.
• Experience with data modeling, data quality management, and data security.
• Excellent problem-solving and analytical skills.
• Strong communication and interpersonal skills.
• Ability to work independently and as part of a team.

Preferred Qualifications / Skills:

• Master’s degree in computer science, Information Technology, or a related field.
• Experience with cloud-based data management platforms, such as AWS, Azure, or GCP.
• Experience with data visualization and reporting tools and Agile methodologies.

Bloomberg runs on data, and in the Data department we're responsible for acquiring, interpreting and supplying data insights to our clients. Our Data teams work to collect, analyse, process, and publish the data which is the backbone of our iconic Bloomberg Terminal -- the data which ultimately moves the financial markets! We’re responsible for delivering this data, news, and analytics through innovative technology -- quickly and accurately.

The Data Management Lab (DML) sits within the Data organization, supporting Data’s pursuit of data management excellence by aligning industry best practices with Bloomberg's established expertise in financial market data. DML empowers our data professionals to make their products “ready-to-use” by promoting increased data discoverability, accessibility, appraisability, interoperability, and analysis-readiness.

As a Data Management Professional, you will play a pivotal role in ensuring the delivery of high-quality data to our clients while driving impactful business decisions. You will be an integral member of Asia-Pacific Quality Methods & Insights team under DML that includes Data Quality, Business Intelligence and Process Engineering, serving as a centre of excellence for the rest of the teams in the Data organisation. A key aspect of this role involves partnering with Data Product and Engineering teams to conduct analysis of operational and product data that is characterized by high volume and variety, generated by a variety of sources in a complex production environment. Specifically, you will leverage your analytical expertise to support the development and adoption of methods that will directly support data-driven decision-making aimed at achieving quality enhancements and process optimisation across the organization. You will also contribute to the ongoing refinement of data management best practices.

We’ll trust you to:

• Lead global initiatives within the realms of data quality, operational efficiency and data management
• Design and run analyses to uncover root causes for data quality issues and make recommendations using techniques in the areas of Bayesian analysis, machine learning, and causal inference
• Formulate and execute optimization analysis to balance trade-offs such as performance, quality and resource usage across data pipelines
• Bring exposure to Operations Research methods such as Linear programming, Queuing theory, and Monte Carlo Simulations
• Bring a basic understanding of data engineering workflows including how data pipelines are orchestrated and various components within the ETL framework
• Deliver actionable insights through advanced analytics, and compelling data storytelling to support business decision making and innovation
• Collaborate with data stakeholders and engineering partners to translate high-impact questions into scalable data science solutions
• Build statistical and analytical capabilities within the team and mentor others

You’ll need to have:

• A Bachelor's degree or higher in Data Science, Economics, Statistics or a relevant STEM field, or equivalent professional work experience
• 1-2 years' experience designing data engineering pipelines
• Proficiency in Python (e.g. PySpark, Pandas, NumPy or Pymc) and SQL
• 3-5 years of experience working in a data quality, data governance, or data management role
• 3-5 years' experience designing research studies using predictive modelling, causal analysis and/or Operations Research methods
• Familiarity with modern data tech stack tools such as dbt, Iceberg, Trino, Airflow, Superset, etc.
• Familiarity with version control systems (e.g., Git) and a collaborative development workflow environment (e.g., GitHub, GitLab)
• Excellent project management skills and the ability to communicate complex findings clearly to both technical and non-technical audiences
• Demonstrated continuous career growth within an organization
• Excellent written and verbal communication skill in English

We'd love to see:

• Experience in Data Management Association (DAMA) and Data Management Capability Assessment Model (DCAM)
• Knowledge of financial markets and Bloomberg products is a plus

The role involves leading the implementation of data governance practices, focusing on critical data elements essential for regulatory reporting, risk assessment, financial disclosures, and KYC/AML compliance. The position includes accountability for governing data used in reports submitted to regulators such as MAS, BNM, APRA, BoT, and others.

This position requires a strong blend of domain expertise in Risk, Finance, Regulatory Reporting, and KYC/AML, combined with a solid technical foundation in data management, ETL processes, and governance platforms like Collibra.

Key Responsibilities

• Support teams in identifying and prioritizing key data elements (KDEs) critical to regulatory and risk reporting (e.g., MAS, BNM, BoT, APRA).
• Collaborate with regulatory reporting teams to harmonize data definitions and resolve ownership or consistency issues.
• Conduct data governance assessments on reports related to IFRS9, Basel III, Liquidity, Credit Risk, and KYC/AML.
• Guide the maintenance of data element traceability from source systems to reporting outputs using governance tools.
• Interpret regulatory requirements from MAS, BNM, APRA, BoT, BCBS 239, and translate them into actionable data governance operations.
• Work with compliance teams to ensure policies align and prepare for audits or inspections.
• Review and assist in remediation of data gaps or inconsistencies in regulatory reporting.
• Facilitate rollout of data governance frameworks, including ownership models, data quality standards, and KDE identification across APAC.
• Engage business, IT, and architecture stakeholders to implement effective governance for critical reporting workflows.
• Support metadata enrichment and key data element cataloging within Collibra for risk and finance datasets.
• Collaborate with tool specialists to manage lineage, ownership, and business definitions in governance platforms.
• Ensure alignment between metadata repositories and actual data usage for regulatory frameworks like Basel, IFRS 9, Liquidity, Large Exposure, etc.
• Act as liaison between business units (Risk, Finance, Compliance) and data governance/IT teams.
• Conduct training sessions and workshops for data stewards and report owners on KDE management, data ownership, and lineage.
• Drive resolution of data-related issues impacting reporting accuracy or regulatory compliance.

Qualifications & Experience

• Bachelor’s degree in Finance, Risk, Business Management, Data Management, or related fields.
• 2 to 7 years’ experience in regulatory reporting, finance, risk, or KYC/AML roles within banking or financial services.
• In-depth knowledge of regulatory frameworks such as BCBS 239, Basel II/III, IFRS 9, MAS, BNM, BoT, and APRA.
• Understanding of the full data flow from source systems through risk calculations to regulatory report submissions.
• Familiarity with data governance concepts and their operationalization, including data ownership, data quality, metadata management, and lineage.
• Experience using data governance platforms such as Collibra is preferred; working knowledge of data management systems, ETL processes, or regulatory reporting tools (e.g., Vermeg, Moody’s, SAS) is advantageous.
• Strong stakeholder management skills to foster collaboration between business and IT teams.
• Excellent documentation and communication skills to define and maintain business metadata and reporting governance.
• Exposure to regulatory audits, remediation efforts, or data governance projects is a plus.
• Relevant certifications such as CDMP, Collibra, or CIMP will be beneficial.

Bloomberg's success hinges on its ability to harness and deliver high-quality data insights to clients.

The Data Management Lab (DML) is the nerve center of Bloomberg's Data organization, where a team of experts works tirelessly to ensure that industry best practices align with Bloomberg's established expertise in financial market data.

As a Lead Data Management Professional, you will play a pivotal role in shaping the company's data management strategy while driving impactful business decisions.

• Drive global initiatives focused on data quality, operational efficiency, and data management across all departments
• Design and execute analytical frameworks to uncover root causes for data quality issues and develop recommendations using Bayesian analysis, machine learning, and causal inference techniques
• Develop optimization strategies to balance trade-offs such as performance, quality, and resource usage across data pipelines
• Apply Operations Research methods like Linear programming, Queuing theory, and Monte Carlo Simulations to drive business outcomes
• Maintain a thorough understanding of data engineering workflows, including how data pipelines are orchestrated and various components within the ETL framework
• Deliver actionable insights through advanced analytics and compelling data storytelling to support business decision making and innovation
• Collaborate with data stakeholders and engineering partners to translate high-impact questions into scalable data science solutions
• Build statistical and analytical capabilities within the team and mentor others

To succeed in this role, you will need:

• A Bachelor's degree or higher in Data Science, Economics, Statistics, or a relevant STEM field, or equivalent professional work experience
• 3-5 years' experience designing data engineering pipelines and optimizing data management processes
• Proficiency in Python (e.g., PySpark, Pandas, NumPy, or Pymc) and SQL
• Experience working in a data quality, data governance, or data management role, with a focus on delivering high-quality data insights to clients
• Familiarity with modern data tech stack tools, such as dbt, Iceberg, Trino, Airflow, Superset, etc.
• Familiarity with version control systems (e.g., Git) and a collaborative development workflow environment (e.g., GitHub, GitLab)
• Excellent project management skills and the ability to communicate complex findings clearly to both technical and non-technical audiences
• Demonstrated continuous career growth within an organization
• Excellent written and verbal communication skills in English

It would be beneficial if you had experience in:

• Data Management Association (DAMA) and Data Management Capability Assessment Model (DCAM)
• Knowledge of financial markets and Bloomberg products

Join a dynamic team as a data management professional at a prestigious organization. As a recent graduate or someone eager to develop hands-on experience in administrative operations and data management, you'll be ideal for this entry-level opportunity.

• Support daily business operations with prior knowledge of the industry, which is highly desirable
• Enter, update, and maintain data in internal systems and databases accurately
• Review and verify data for accuracy and completeness
• Input and update invoices into the company's database system efficiently
• Verify invoice details such as amounts, dates, and payment terms
• Communicate with suppliers or internal departments to resolve discrepancies or missing information
• Maintain accurate and organized records of invoices and payments
• Assist in reconciling invoices and supporting documentation
• Organize and manage physical and digital files and records
• Assist in preparing reports and summaries from entered data
• Provide general administrative support including filing, photocopying, and data entry
• Respond to inquiries and assist with internal data requests
• Assist with organizing and managing office documentation and scheduling tasks

This role requires a highly organized and detail-oriented individual who can work effectively in a fast-paced environment.

Job Summary:

The Administrative Support Assistant will provide data management, analytics, and strategic guidance to support agency leaders and financial consultants.

Key responsibilities include assisting Recruitment Managers in day-to-day operations, conducting fitness and propriety assessments, participating in financial consultant interviews, and tracking recruitment applications.

Main Tasks:

• Data management
• Analytics and reporting
• Strategic guidance
• Support for Recruitment Managers
• Fitness and propriety assessments
• Financial consultant interviews
• Tracking recruitment applications

Requirements:

• Diploma in a related discipline
• 1 year of relevant experience

Benefits:

A motivated and skilled team player who can start immediately or within short notice.

Contact Information:

The successful candidate will be responsible for ensuring that service level agreements (SLAs) and key performance indicators (KPIs) are consistently met, reviewed, and revised as needed.

Key responsibilities include:

• Overseeing the data management team to guarantee timely and accurate processing of human resources data.
• Ensuring SLAs and KPIs are consistently met, reviewed, and revised as needed.
• Acting as a subject matter expert in HR data management, overseeing and managing agreed HR processes, and ensuring services are delivered in line with relevant legislation requirements.
• Developing and maintaining good working relationships with cross-functional teams, internal shared services, HRIS teams, and various business partners.
• Analyzing work process design to implement improved processes and suggesting appropriate solutions or improvements to raise employee experience and efficiency.

Requirements:

• Minimum 3 years of human resources experience, preferably with at least 1 year of supervisory management experience.
• Past working experience in payroll administration in healthcare is an added advantage.
• Competent in HR system and MS office.