Singapore-based, APAC-wide BI leadership role that blends governance, platform development, and strategic storytelling. You own the Commercial BI stack end-to-end—from CV Portal and Data Cube to...
read more »
Singapore-based, APAC-wide BI leadership role that blends governance, platform development, and strategic storytelling. You own the Commercial BI stack end-to-end—from CV Portal and Data Cube to automating reports and driving adoption, with PoCs in Tableau and governance via Collibra. The job leans heavy into harmonization, data governance, and a regional SLA-driven Shared Service Center for Cognos report extraction, plus cross-country influence and executive storytelling using the APAC Balanced Scorecard. It demands expert Tableau/Power BI, data modeling, ERP chops (Oracle, SAP), and comfort with data lakes and data marts; SQL stamina and strong Excel/PowerPoint are assumed. Preferred skills include Python or R and perhaps Asian language fluency to actually talk to markets. Travel is modest but real. The big risk: scope creep, relentless standards maintenance, and a management layer heavy on governance rather than raw data wrangling; compensation is not disclosed.
Finance Analyst role in Controlling hinges on month-end closing in SAP FI/CO, P&L and cost analysis, and churns out Excel-driven reports for Sales and Marketing. The tech stack is SAP and Excel,...
read more »
Finance Analyst role in Controlling hinges on month-end closing in SAP FI/CO, P&L and cost analysis, and churns out Excel-driven reports for Sales and Marketing. The tech stack is SAP and Excel, with VBA a plus; no data warehouse or modern tooling evident. Requires 3–6 years, strong SAP, advanced Excel (SUMIFS, INDEX), pivot tables; knowledge of accounting principles; CPA/ACCA a plus. Opportunities lie in system configuration tweaks, cost allocation reviews, and cross‑functional forecasting, but risk is bang-for-the-buck Excel glare and late-stage deadline pressure. Lacks scalable data engineering or analytics tooling beyond Excel; career stability if you want a deep financial consolidation role, but not a data engineering career ladder.
O'Reilly: Building AI Agents with Model Context Protocol (MCP)
Design and implement composable agent architectures using MCP. Understand the MCP architecture and how it enables AI applications to access external context. Build MCP servers that expose tools, resources, and prompts to LLMs.
Territory Management and Sales Incentive Analyst
@ sartorius
SG | 2025-12-28
At Sartorius, the Territory Management & Incentive Compensation Analyst wears the data hat across global sales, shaping equitable territories, automated payout calculations, and governance of...
read more »
Territory Management and Sales Incentive Analyst
sartorius
(SG) FULL TIME | JOB LISTED
At Sartorius, the Territory Management & Incentive Compensation Analyst wears the data hat across global sales, shaping equitable territories, automated payout calculations, and governance of quality controls. The role marries SQL, Python, and analytics with SAP BW/HANA, Snowflake, and Tableau to model scenarios, monitor KPIs, and automate data pipelines, while coordinating with Sales, HR, Finance, IT, and the Workers’ Council. What stands out is genuine cross-functional impact—how incentive plans tie to real revenue, not buzzwords—paired with a strong emphasis on data quality and audit readiness. It’s less a pure engineering fix and more a governance-heavy analytics engineer position in a life-sciences giant listed on the DAX. The downside: heavy stakeholder management, potential process drag, and no salary detail in the posting; good operators will relish the optimization grind.
Leica Microsystems, a Danaher company, offers a supply chain analyst role focused on purchasing in a life sciences manufacturing context. The job foregrounds demand and supply coordination,...
read more »
Leica Microsystems, a Danaher company, offers a supply chain analyst role focused on purchasing in a life sciences manufacturing context. The job foregrounds demand and supply coordination, supplier performance management, inventory control, and data-driven process improvement via dashboards and DBS methodologies. You’ll be on-site in Singapore, collaborating across Order Fulfilment, Planning, and Production to prevent stockouts and chase continuous improvement with lean tools. Proficiency in Excel, Power BI, and PowerPoint is required; SAP or Kanban experience is a plus. What stands out is the blend of heritage and modern data discipline in a sector that saves lives, but the role risks being a process-heavy grind unless you can translate dashboards into actionable decisions. The lack of explicit salary info and the DBS constraint are notable market realities.
On paper, a Pall QA Engineer role in Singapore anchors quality control across incoming, in-process, and final release, with heavy reliance on established quality tools and Lean/Six Sigma. The tech...
read more »
On paper, a Pall QA Engineer role in Singapore anchors quality control across incoming, in-process, and final release, with heavy reliance on established quality tools and Lean/Six Sigma. The tech stack isn’t code, it’s statistics and process control: FMEA, DOE, SPC, MSA, 5Why, 8D, CAPA, and the SPACE system, plus SAP for documentation. It’s a data-lean role by typical data engineering standards, but rigorous about root cause and preventive action; success hinges on cross-functional alignment with Production, Manufacturing Engineering, Materials/Warehouse, and Customer Quality. Expected to handle external complaints, audits (ISO, customer), and drive continuous improvement. The market note: stable but narrow data analytics scope; good for discipline and operational maturity, less for building data platforms. Space and attention to traceability are real positives; the lack of true programming or scalable data tooling is the pitfall.
Here’s a finance role dressed as an engineering problem: own end-to-end billing, reconciliations, and monthly reports, while driving improvements to ERP systems and controls. The tech footprint...
read more »
Here’s a finance role dressed as an engineering problem: own end-to-end billing, reconciliations, and monthly reports, while driving improvements to ERP systems and controls. The tech footprint leans on SAP or Oracle, heavy Excel-based analysis, and the appetite for dashboards and management reporting. It’s a governance- and audit-heavy setup that rewards rigor and error-spotting, not flashy data science. Expect to translate financial requirements into system configurations, test user acceptance during upgrades, and build analytics that illuminate variance, cash flow, and trends. The biggest challenges are aligning meticulous accounting standards with fast-paced operations, taming data quality across systems, and avoiding spreadsheet chaos. Opportunities lie in automating repetitive tasks, tightening controls, and delivering actionable insights for executives, though this role will rarely resemble modern data engineering pipelines.
O'Reilly: Building AI Agents with Model Context Protocol (MCP)
Design and implement composable agent architectures using MCP. Understand the MCP architecture and how it enables AI applications to access external context. Build MCP servers that expose tools, resources, and prompts to LLMs.
Associate AI Developer/ AI Developer (Full stack) - Artificial Intelligence, SAP Labs Singapore
@ sap
SG | 2025-12-25
SAP Labs Singapore advertises an Associate AI Developer role nestled inside a global ERP behemoth’s AI push. The job promises multi-terabyte data pipelines, cloud-based AI work, and collaboration...
read more »
Associate AI Developer/ AI Developer (Full stack) - Artificial Intelligence, SAP Labs Singapore
sap
(SG) INTERNSHIP | JOB LISTED
SAP Labs Singapore advertises an Associate AI Developer role nestled inside a global ERP behemoth’s AI push. The job promises multi-terabyte data pipelines, cloud-based AI work, and collaboration with AI scientists, data engineers, and DevOps, which sounds grand until you ask what 'top-notch AI systems' actually means and how fixed timelines mesh with exploratory research. You’ll need Java, Scala, Go, Python, Node.js or C++, plus Hadoop, Spark and Kafka, with Spring, Flask, AngularJS and more, all deployed on Docker/Kubernetes and cloud platforms (SAP BTP, AWS, Azure, GCP). The market is crowded with similar roles; SAP’s advantage is scale and enterprise responsibility, but the risk is vague success criteria and bureaucratic pace. Singapore-specific work rights gate adds friction but aligns with its engineering hub ambitions.
This position at Deloitte's Artificial Intelligence & Data team in Singapore is ideal for fresh graduates or recent entrants looking to build a technical foundation in data engineering amidst an...
read more »
This position at Deloitte's Artificial Intelligence & Data team in Singapore is ideal for fresh graduates or recent entrants looking to build a technical foundation in data engineering amidst an avalanche of corporate flummery. Candidates will wield SQL, Oracle, and an assortment of Big Data tools while navigating data integration complexities. The promise of working with industry giants might be enticing, but the reality of rampant buzzword usage—'cognitive technologies'—raises eyebrows. With a focus on emerging technologies, the role offers genuine opportunities for growth and learning, assuming you can survive the inevitable bureaucratic layers. The requirement for knowledge in AI, ML, and various development methodologies indicates a demanding expectations set, which, combined with a sprinkle of empty panegyrics, might leave you levelling up your coffee consumption just to stay afloat.
Singapore Airlines is hunting a seasoned data architect/engineer to own data cleansing, migration, and analytics in a broad SAP-centric stack tied to a S/4HANA implementation and a corporate data...
read more »
Singapore Airlines is hunting a seasoned data architect/engineer to own data cleansing, migration, and analytics in a broad SAP-centric stack tied to a S/4HANA implementation and a corporate data lake. Expect heavy SAP work: S/4HANA analytics, BW/4HANA, BW on HANA, and SAP Analytics Cloud, with DWC and BW Bridge as connective tissue; ETL through SDI and Data Services; native HANA modeling; and connections to SuccessFactors, Ariba, and Concur, plus AWS S3/Redshift and Tableau for visualization. The role is an individual contributor but mission-critical: define the analytics architecture, vet platforms, and drive delivery with internal and external teams. Good fit for someone who likes end-to-end project life cycles and governance in a large enterprise, but beware the SAP-first constraint, scope creep, and a potentially protracted migration path before any payoff shows.
Director – Global People Services Solutions (Innovation, AI, Productivity Improvement & Data)
@ micron-technology
SG | 2025-12-23
Micron's People Services Solutions Director is a strategic, cross-functional lead who claims to reinvent HR through AI, automation and analytics. The role blends process excellence with data...
read more »
Singapore-based, APAC-wide BI leadership role that blends governance, platform development, and strategic storytelling. You own the Commercial BI stack end-to-end—from CV Portal and Data Cube to automating reports and driving adoption, with PoCs in Tableau and governance via Collibra. The job leans heavy into harmonization, data governance, and a regional SLA-driven Shared Service Center for Cognos report extraction, plus cross-country influence and executive storytelling using the APAC Balanced Scorecard. It demands expert Tableau/Power BI, data modeling, ERP chops (Oracle, SAP), and comfort with data lakes and data marts; SQL stamina and strong Excel/PowerPoint are assumed. Preferred skills include Python or R and perhaps Asian language fluency to actually talk to markets. Travel is modest but real. The big risk: scope creep, relentless standards maintenance, and a management layer heavy on governance rather than raw data wrangling; compensation is not disclosed.
Finance Analyst role in Controlling hinges on month-end closing in SAP FI/CO, P&L and cost analysis, and churns out Excel-driven reports for Sales and Marketing. The tech stack is SAP and Excel, with VBA a plus; no data warehouse or modern tooling evident. Requires 3–6 years, strong SAP, advanced Excel (SUMIFS, INDEX), pivot tables; knowledge of accounting principles; CPA/ACCA a plus. Opportunities lie in system configuration tweaks, cost allocation reviews, and cross‑functional forecasting, but risk is bang-for-the-buck Excel glare and late-stage deadline pressure. Lacks scalable data engineering or analytics tooling beyond Excel; career stability if you want a deep financial consolidation role, but not a data engineering career ladder.
Territory Management and Sales Incentive Analyst
sartorius
(SG) FULL TIME | JOB LISTED
At Sartorius, the Territory Management & Incentive Compensation Analyst wears the data hat across global sales, shaping equitable territories, automated payout calculations, and governance of quality controls. The role marries SQL, Python, and analytics with SAP BW/HANA, Snowflake, and Tableau to model scenarios, monitor KPIs, and automate data pipelines, while coordinating with Sales, HR, Finance, IT, and the Workers’ Council. What stands out is genuine cross-functional impact—how incentive plans tie to real revenue, not buzzwords—paired with a strong emphasis on data quality and audit readiness. It’s less a pure engineering fix and more a governance-heavy analytics engineer position in a life-sciences giant listed on the DAX. The downside: heavy stakeholder management, potential process drag, and no salary detail in the posting; good operators will relish the optimization grind.
Leica Microsystems, a Danaher company, offers a supply chain analyst role focused on purchasing in a life sciences manufacturing context. The job foregrounds demand and supply coordination, supplier performance management, inventory control, and data-driven process improvement via dashboards and DBS methodologies. You’ll be on-site in Singapore, collaborating across Order Fulfilment, Planning, and Production to prevent stockouts and chase continuous improvement with lean tools. Proficiency in Excel, Power BI, and PowerPoint is required; SAP or Kanban experience is a plus. What stands out is the blend of heritage and modern data discipline in a sector that saves lives, but the role risks being a process-heavy grind unless you can translate dashboards into actionable decisions. The lack of explicit salary info and the DBS constraint are notable market realities.
On paper, a Pall QA Engineer role in Singapore anchors quality control across incoming, in-process, and final release, with heavy reliance on established quality tools and Lean/Six Sigma. The tech stack isn’t code, it’s statistics and process control: FMEA, DOE, SPC, MSA, 5Why, 8D, CAPA, and the SPACE system, plus SAP for documentation. It’s a data-lean role by typical data engineering standards, but rigorous about root cause and preventive action; success hinges on cross-functional alignment with Production, Manufacturing Engineering, Materials/Warehouse, and Customer Quality. Expected to handle external complaints, audits (ISO, customer), and drive continuous improvement. The market note: stable but narrow data analytics scope; good for discipline and operational maturity, less for building data platforms. Space and attention to traceability are real positives; the lack of true programming or scalable data tooling is the pitfall.
Here’s a finance role dressed as an engineering problem: own end-to-end billing, reconciliations, and monthly reports, while driving improvements to ERP systems and controls. The tech footprint leans on SAP or Oracle, heavy Excel-based analysis, and the appetite for dashboards and management reporting. It’s a governance- and audit-heavy setup that rewards rigor and error-spotting, not flashy data science. Expect to translate financial requirements into system configurations, test user acceptance during upgrades, and build analytics that illuminate variance, cash flow, and trends. The biggest challenges are aligning meticulous accounting standards with fast-paced operations, taming data quality across systems, and avoiding spreadsheet chaos. Opportunities lie in automating repetitive tasks, tightening controls, and delivering actionable insights for executives, though this role will rarely resemble modern data engineering pipelines.
Associate AI Developer/ AI Developer (Full stack) - Artificial Intelligence, SAP Labs Singapore
sap
(SG) INTERNSHIP | JOB LISTED
SAP Labs Singapore advertises an Associate AI Developer role nestled inside a global ERP behemoth’s AI push. The job promises multi-terabyte data pipelines, cloud-based AI work, and collaboration with AI scientists, data engineers, and DevOps, which sounds grand until you ask what 'top-notch AI systems' actually means and how fixed timelines mesh with exploratory research. You’ll need Java, Scala, Go, Python, Node.js or C++, plus Hadoop, Spark and Kafka, with Spring, Flask, AngularJS and more, all deployed on Docker/Kubernetes and cloud platforms (SAP BTP, AWS, Azure, GCP). The market is crowded with similar roles; SAP’s advantage is scale and enterprise responsibility, but the risk is vague success criteria and bureaucratic pace. Singapore-specific work rights gate adds friction but aligns with its engineering hub ambitions.
This position at Deloitte's Artificial Intelligence & Data team in Singapore is ideal for fresh graduates or recent entrants looking to build a technical foundation in data engineering amidst an avalanche of corporate flummery. Candidates will wield SQL, Oracle, and an assortment of Big Data tools while navigating data integration complexities. The promise of working with industry giants might be enticing, but the reality of rampant buzzword usage—'cognitive technologies'—raises eyebrows. With a focus on emerging technologies, the role offers genuine opportunities for growth and learning, assuming you can survive the inevitable bureaucratic layers. The requirement for knowledge in AI, ML, and various development methodologies indicates a demanding expectations set, which, combined with a sprinkle of empty panegyrics, might leave you levelling up your coffee consumption just to stay afloat.
Singapore Airlines is hunting a seasoned data architect/engineer to own data cleansing, migration, and analytics in a broad SAP-centric stack tied to a S/4HANA implementation and a corporate data lake. Expect heavy SAP work: S/4HANA analytics, BW/4HANA, BW on HANA, and SAP Analytics Cloud, with DWC and BW Bridge as connective tissue; ETL through SDI and Data Services; native HANA modeling; and connections to SuccessFactors, Ariba, and Concur, plus AWS S3/Redshift and Tableau for visualization. The role is an individual contributor but mission-critical: define the analytics architecture, vet platforms, and drive delivery with internal and external teams. Good fit for someone who likes end-to-end project life cycles and governance in a large enterprise, but beware the SAP-first constraint, scope creep, and a potentially protracted migration path before any payoff shows.
Director – Global People Services Solutions (Innovation, AI, Productivity Improvement & Data)
micron-technology
(SG) FULL TIME | JOB SUSPENDED
Micron's People Services Solutions Director is a strategic, cross-functional lead who claims to reinvent HR through AI, automation and analytics. The role blends process excellence with data governance, demanding hands-on appetite for piloting generative AI, chatbots and RPA while keeping privacy and bias squarely in sight. The candidate must chart an AI strategy across talent acquisition, employee support and workforce planning, then prove it with measurable productivity gains and service improvements. The toolkit reads like a wish list: Copilot, ServiceNow, Power BI, Tableau, Lean Six Sigma and Agile, plus familiarity with Workday or SAP SuccessFactors. What stands out is the rare combination of operational scale and data-driven mandate in HR; what bothers is the breadth of accountability in a single role and the lack of explicit compensation detail. It's a leadership slot with obvious visibility but real execution risk.
