Data Engineer at Visa

Singapore, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

24 Jun, 26

Salary

0.0

Posted On

26 Mar, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Pipelines, Semantic Models, BI Solutions, GenAI, RAG, Prompt Engineering, SQL, Hive, PySpark, Lakehouse, Power BI, Fabric Semantic Models, SSAS Tabular, DAX, CI/CD, Great Expectations

Industry

IT Services and IT Consulting

Description

Company Description Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters — to you, to your community, and to the world. Progress starts with you. Job Description We are building a next‑generation, business‑centric data intelligence and AI foundation that fuels Finance—from FP&A intelligence to product, compliance, and controllership decision‑making. As a Data Engineer of the Finance Technology – Data Intelligence organization, you will architect scalable data pipelines, semantic models, and end‑to‑end BI solutions, while safely operationalizing GenAI capabilities such as RAG, prompt engineering, evaluation frameworks, and agent‑based workflows. You will collaborate closely with analysts, data scientists, and engineering partners to deliver secure, reliable, auditable, and reusable data and AI services that materially enhance decision quality, automation, and speed across Finance. Responsibilities: Build the data foundation Collaborate with Data Analysts, Data Scientists, Software Engineers, and cross-functional partners to design, build, and deploy scalable data pipelines that deliver high‑quality, governed analytical datasets across Finance domains. Engineer high‑quality batch/streaming data pipelines (SQL/Hive/PySpark) across Lake/Lakehouse to power curated finance domain marts and a governed semantic layer. Design dimensional/semantic models that enable self‑service analytics (Power BI / Fabric semantic models / SSAS Tabular) with performant DAX measures and row‑level security. Operationalize Gen AI for Finance Ship production‑grade Gen AI features (retrieval‑augmented generation, prompt‑chaining/agents) on governed datasets - implement vectorization strategies and chunking that respect PII/SOX controls. Partner with DS/ML to train/fine‑tune and evaluate models - harden prompt templates, guardrails, and content filters - track hallucination, toxicity, and retrieval metrics (precision/recall, hit@k). Build reusable components (prompt libraries, evaluation harnesses, vector store abstractions) and integration SDKs/APIs for reuse across Finance use cases. Platform, reliability & DevOps Implement CI/CD for data & AI (Git, Azure DevOps/GitHub Actions), data quality tests (Great Expectations or equivalent), and model/data deployment automation (MLflow/Fabric/Azure ML). Define observability (lineage, drift, freshness, cost) with alerts/SLAs - drive continuous hardening for performance (SQL/DAX tuning), cost efficiency, and reliability. Analytics enablement Deliver high‑impact dashboards/scorecards (Power BI/Tableau) and governed certified datasets - coach analysts on best‑practice modeling and performance tuning. Risk, governance & documentation Embed privacy‑by‑design (PII masking, purpose limitation), finance controls (SOX, audit trails), and robust documentation (runbooks, data dictionaries, model cards). This is a hybrid position. Expectation of days in the office will be confirmed by your Hiring Manager. Qualifications Preferred Qualifications: Bachelors degree in Engineering with Honors in Data Science or Computer Science is required, along with 1+ years of hands-on experience building large scale data processing platforms Strong understanding of data warehousing concepts, including ER data modeling, data warehouse architecture, feature engineering, and solid knowledge of the Big Data ecosystem and its 5 Vs. 1+ years of practical experience using SQL/Hive/PySpark for data extraction, aggregation, optimization, and storage on Hadoop technologies (Spark, Tez, MR) and cloud platforms 1+ years of applied GenAI engineering experience, including production grade GenAI features such as RAG over enterprise data, prompt engineering, evaluation, guardrails, and familiarity with LLMs, vectorization, chunking, and orchestration frameworks (LangChain). Ability to build reusable components—including prompt libraries, evaluation frameworks, vector store abstractions—and integration SDKs/APIs to enable reuse across Finance scenarios. 1+ years of hands-on experience delivering end to end Business Intelligence solutions, with an understanding of ETL strategies and the ability to contribute to data model decisions for reporting. Working knowledge of Machine Learning, Deep Learning, GenAI, and MLOps is a strong advantage. Familiarity with Data administration (YARN, Splunk, Profiler, Perfmon, security architecture, user provisioning, audit, etc.) is preferred. Exposure to Finance Data Analytics or finance domain is an added advantage. Additional Information Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law. Job Family Group: Technology and Operations

Responsibilities

The role involves architecting scalable data pipelines, semantic models, and end-to-end BI solutions while safely operationalizing GenAI capabilities like RAG and prompt engineering for Finance decision-making. Responsibilities include building data foundations, operationalizing GenAI features, implementing platform reliability practices, and enabling analytics through dashboards and governed datasets.