Data Engineer - La Valle Agordina
1 giorno fa

Descrizione del lavoro
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.
Key Responsibilities
Data Engineering
- 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.
AI / ML Engineering (Applied)
- 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.
Applied Data Science
- 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.
Platform & Collaboration
- 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.
Required Skills & Experience
- 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.
Cloud & AI Platform Experience
- 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.
Nice to Have
- 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.
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