Data Engineer Project-Based Consultant

Job Title: Data Engineer (Project-Based Consultant)

Role Summary

• The Data Engineer is responsible for designing, building, and operating data pipelines and AI-enabled capabilities for an in-house, cloud-native enterprise system undergoing modernization.
• This role combines strong data engineering fundamentals with applied AI/ML engineering, leveraging managed cloud data and AI services to deliver analytics, insights, and AI-assisted features that are production-ready, governed, explainable, and auditable.
• The role focuses on practical, applied AI (not research) and works closely with application developers, architects, QA, and product teams to integrate data and AI into core business workflows.
  
  

• Design and build batch and streaming data pipelines for enterprise applications.
• Ingest, transform, and model data into analytics- and AI-ready datasets.
• Ensure data quality, validation, consistency, and lineage across pipelines.
• Design data models to support reporting, analytics, and AI feature development.
• Optimize data pipelines for performance, scalability, and cost efficiency.
• Support data integration between operational systems and analytics platforms.
• Strong skills in Python and SQL.
• Experience with ETL/ELT, data warehousing, and pipeline automation.
• Hands-on with BigQuery, Datastream, Cloud Composer, Dataflow/Dataproc.
• Knowledge in database design and data modeling.
  
  

• Develop and deploy AI-enabled capabilities using managed cloud AI/ML services.
• Train, evaluate, and deploy models using cloud-native ML platforms (e.g., AutoML, SQL-based ML).
• Integrate AI outputs (scores, predictions, classifications) into application workflows via APIs.
• Monitor model behavior, performance, and data drift.
• Ensure AI features are explainable, auditable, and user-controllable.
• Work with architects to define clear boundaries between rules-based logic and AI-assisted decisioning.
• Build end-to-end ML workflows using Vertex AI (training → evaluation → deployment → monitoring).
• Develop ML models for:
• Borrower verification
• Credit scoring
• Affordability validation
• Risk & fraud detection
• Forecasting (clients, portfolio, PAR trends)
• Collections and repayment behavior
• Implement MLOps automation (model versioning, CI/CD for ML, model monitoring).
• Use BigQuery ML to create fast, scalable models when deep ML is not required.
  
  

• Perform exploratory data analysis (EDA) to understand trends and anomalies.
• Conduct feature engineering and create AI-ready datasets.
• Use statistical and ML techniques (regression, clustering, classification, anomaly detection, time-series forecasting).
• Evaluate model performance using metrics such as ROC, AUC, RMSE, accuracy, precision/recall.
• Translate field operations needs into measurable AI models.
• Provide insights to support Ops, Finance, and Audit decision-making.
  
  

• Implement solutions using public cloud data and AI services, primarily on Google Cloud Platform.
• Collaborate with frontend and backend developers to enable data- and AI-driven features.
• Support QA in validating data accuracy and AI output correctness.
• Contribute to documentation covering data pipelines, AI models, and usage guidelines.
• Follow architecture, security, and governance standards defined for the platform.
  
  

• 5–8+ years of experience in data engineering, analytics engineering, or related roles.
• Strong hands-on experience building production-grade data pipelines.
• Practical experience with applied machine learning (not academic research).
• Strong SQL skills and working knowledge of Python or similar languages.
• Experience integrating data and ML outputs into applications.
• Solid understanding of data quality, governance, and lifecycle management.
• Ability to communicate technical concepts clearly to technical and non-technical stakeholders.
  
  

• Google Cloud Platform experience is preferred, particularly with data and AI services.
• Strong equivalent experience in AWS or Azure is acceptable, with the ability to adapt.
• Experience using managed AI/ML services rather than building models from scratch.
  
  

• Experience working with enterprise or regulated systems.
• Exposure to AI governance, explainability, or model monitoring.
• Experience supporting legacy-to-modern data migrations.
• Familiarity with CI/CD practices for data or ML pipelines.
• Must be willing to work onsite at our Ortigas office and be comfortable working UK time hours.