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About the role
Visa is hiring a Data Engineer to join its Finance Technology – Data Intelligence team, building a business-centric data intelligence and AI foundation that supports Finance decision-making. You will work alongside analysts, data scientists and engineering partners to deliver secure, reliable and reusable data and AI services. This role suits someone with strong data engineering fundamentals who is comfortable working across pipelines, semantic models and production GenAI features.
What you'll do
Work with Data Analysts, Data Scientists, Software Engineers and cross-functional partners to design, build and deploy scalable data pipelines that produce governed analytical datasets for Finance domains.
Engineer batch and streaming pipelines using SQL, Hive and PySpark across Lake and Lakehouse environments to power curated finance domain marts and a governed semantic layer.
Design dimensional and semantic models that support self-service analytics, including Power BI, Fabric semantic models and SSAS Tabular, with performant DAX measures and row-level security.
Ship production-grade GenAI features such as retrieval-augmented generation and prompt-chaining or agents on governed datasets, implementing vectorization and chunking that respect PII and SOX controls.
Partner with data science and machine learning teams to train, fine-tune and evaluate models, harden prompt templates, guardrails and content filters, and track hallucination, toxicity and retrieval metrics.
Build reusable components such as prompt libraries, evaluation harnesses and vector store abstractions, plus integration SDKs and APIs for reuse across Finance use cases.
Implement CI/CD for data and AI using Git and Azure DevOps or GitHub Actions, data quality tests with Great Expectations or equivalent, and model or data deployment automation with MLflow, Fabric or Azure ML.
Define observability covering lineage, drift, freshness and cost with alerts and SLAs, and drive continuous hardening for performance, cost efficiency and reliability.
Deliver dashboards and scorecards in Power BI or Tableau along with governed certified datasets, and coach analysts on modelling and performance tuning.
Embed privacy-by-design such as PII masking and purpose limitation, finance controls including SOX and audit trails, and documentation such as runbooks, data dictionaries and model cards.
Good to know
This is a hybrid position; Visa requires at least 3 days in office, with the specific days confirmed by your Hiring Manager.
Requirements
Bachelor's degree, or 3+ years of relevant work experience
Bachelor's degree in Engineering with Honours in Data Science or Computer Science
1+ years of hands-on experience building large-scale data processing platforms
Strong understanding of data warehousing concepts including ER data modelling, data warehouse architecture, feature engineering and the Big Data ecosystem
1+ years of practical experience with SQL, Hive or PySpark for extraction, aggregation, optimization and storage on Hadoop technologies and cloud platforms
1+ years of applied GenAI engineering experience including production RAG over enterprise data, prompt engineering, evaluation, guardrails and familiarity with LLMs, vectorization, chunking and orchestration frameworks such as LangChain
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