Ermar Palma
VerifiedData Engineering Manager · AI Analytics
PhilippinesLead
Measured business impact
Outcomes reported against approved AI implementations, presented for executive review.
- Hours Automated
- 1 hours
- Cost Savings
- —
- Revenue Impact
- —
- AI Deployments
- 1
- Published Case Studies
- 0
Operational hours recovered through verified AI deployments.
Verified operating cost reductions delivered for client organisations.
Revenue created or protected through deployed AI solutions.
Implementations reviewed and approved against the Evidence Standard.
Documented implementations with published business outcomes.
- VerificationVerified
- SpecialisationAI Analytics
- AI Deployments1
- Certifications2
- Industries Served5
- Response TimeWithin 24 hours
- AvailabilityAvailable Within 2 Weeks
- LocationPhilippines
How this is verifiedWhat the platform has established, and what remains builder-declared.
- Identity and organisation
- Identity checks confirm that the person presenting this profile controls the account and the professional identifiers attached to it, such as a LinkedIn profile. Organisation membership is confirmed by the organisation, not self-declared by the builder.
- AI Deployments
- An approved AI Deployment is an implementation record the builder submitted and the platform reviewed against the Evidence Standard before publication. Approval confirms the record is complete, coherent and consistent with the supporting material provided — it is not an audit of the client's systems.
- Business outcomes
- Outcome figures are recorded by the builder on the deployment or case study that produced them, and are reviewed at publication. Aggregates shown on this profile are calculated from those approved records only.
- Evidence
- Supporting evidence comes from the records the builder published on this platform: approved AI Deployments, published AI Case Studies, verification outcomes and organisation participation.
- Currency
- Older evidence continues to count and is never removed. Recency is presented separately through Professional Currency so that current expertise can be assessed alongside historical work.
- Traceability
- Every figure on this profile can be followed to the record that produced it. Open the linked AI Deployment or AI Case Study to read the outcome exactly as it was reported.
The platform does not independently audit client systems or financial records. How this is verified
Top business outcomes
Reported by the builder on the record that produced them, in the original reporting period. Nothing is annualised or converted.
- Hours Automated1 hours
period not specified
Automated 1 hours (period not specified)
Builder-reported outcomes, recorded on deployment and case study records that AI Deploy Network reviewed before publication. AI Deploy Network does not currently capture a measurement basis or calculation methodology for reported outcomes, so none is shown.
Capabilities: declared and demonstrated
1 of 31 listed capabilities are supported by an approved deployment or published case study on this platform. The rest are Builder-declared and are not presented as verified.
Implementation capability
- AI Analytics
- Forecasting & Prediction1
Technologies
- .NET
- AWS
- Amazon Bedrock
- Amazon S3
- Apache Airflow
- Apache Kafka
- Auth0
- Azure AI Foundry
- BigQuery
- CapCut
- Claude Agent SDK
- Claude Code
- Confluent
- Cosmos DB
- Databricks
- Docker
- Docker Swarm
- DynamoDB
- Google Analytics
- Google Cloud
- Hugging Face AutoTrain
- Kubernetes
- Microsoft Azure
- Microsoft Entra ID
Industries
- FinTech
- Cloud & Infrastructure Providers
- Data & Analytics
- Software & SaaS
- IT Services & Systems Integration
AI Deployments (1)
Technical implementation records: what was built, the technologies used and the scope delivered. Each was reviewed before publication.
Professional Certifications
Some recognitionSnowPro Core Certification
Snowflake
Issue date not specified
Lean Six Sigma Yellow Belt
Nievgen
Issue date not specified
About
Data Engineering Manager with 15+ years of IT experience, including 9+ years building enterprise data platforms and 8+ years designing cloud-native AWS solutions. Extensive expertise in architecting modern data ecosystems using Snowflake, AWS, Azure Data Factory, dbt, Kafka, Python, and SQL. Proven leader in building high-performing engineering teams, driving cloud migration strategies, establishing engineering standards, and delivering scalable, secure, and governed data platforms that power enterprise analytics and business growth. Experienced in leveraging AI-powered engineering tools including Snowflake Cortex/CoCo, Atlassian Rovo, Claude, GitHub Copilot, and ChatGPT to accelerate software development, data engineering, documentation, code reviews, troubleshooting, and solution design, improving team productivity and delivery quality.
Ready to discuss your AI implementation?
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