Engineering Manager, Data Engineering
TRM LabsSalary not specified
Full Time
Expert & Leadership (9+ years)
Not SpecifiedCompensation
Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level
Full TimeJob Type
UnknownVisa
Fintech, Payments, TechnologyIndustries
Requirements
- 12 or more years of work experience with a Bachelor’s Degree or at least 10 years of work experience with an Advanced degree (e.g. Masters/MBA/JD/MD), or a minimum of 5 years of work experience with a PhD
- Preferred: 15 or more years of experience with a Bachelor’s Degree or 12 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, or MD), PhD with 9+ years of experience
- Bachelor’s degree with twelve or more years of experience, or a Master’s/PhD with relevant experience in data engineering, computer science, or related fields
- Strong hands-on experience with big data platforms, distributed computing, real-time systems, and open-source technologies relevant to analytics and engagement (e.g., Hadoop, Spark, Flink, Kafka, Hive)
- Hands-on coding and production deployment expertise in Python, Java, SQL, Scala, Go, or C
- Track record integrating AI, ML, and GenAI into analytics and marketing platforms at scale
- Deep understanding of data architecture, pipeline optimization, testing, monitoring, and data governance for marketing and consumer engagement applications
Responsibilities
- Architect, design, and deliver high-performing, extensible, and reusable data engineering systems that enable real-time and batch marketing analytics and engagement solutions at scale
- Define technical standards, frameworks, and best practices for resilient and secure data pipeline development, ensuring adherence to all compliance, privacy, and security requirements for marketing and customer engagement datasets
- Build and maintain robust ETL and ELT processes and data marts, leveraging big data and cloud technologies such as Spark, Hive, Hadoop, Kafka, AWS, and Azure
- Deliver and monitor quality, completeness, and performance of marketing data platforms through instrumentation, benchmarking, and automated testing
- Transform Visa’s digital offerings by integrating AI, ML, and GenAI capabilities for next-level campaign optimization, personalization, and customer loyalty measurement
- Lead and influence multiple teams with a culture of innovation and drive for engineering excellence, mentoring and developing talent to sustain high-functioning, purpose-driven teams
- Collaborate cross-functionally with product owners, business partners, and cybersecurity experts to clarify business requirements and translate them into scalable technical solutions for global digital engagement
- Explore, map, and negotiate access to new data sources. Champion a data-driven culture through training, knowledge sharing, and internal documentation
- Continuously stay ahead of emerging trends, industry best practices, and innovative data technologies to strengthen the competitiveness of Visa’s DME value-added products
Skills
Spark
Hive
Hadoop
Kafka
AWS
Azure
ETL
ELT
Data Pipelines
Big Data
AI
ML
GenAI
Visa
Global digital payment network provider
About Visa
Visa operates a global digital payment network that facilitates electronic payments for millions of people daily. The company connects consumers, businesses, financial institutions, and governments, allowing them to make transactions using Visa cards. Each time a card is used, Visa earns money through transaction, service, and data processing fees. Unlike many competitors, Visa focuses on expanding access to financial services for underserved communities and supporting local economies. The company's goal is to promote financial inclusivity and drive sustainable commerce, ensuring that more people can participate in the global economy.
San Francisco, CaliforniaHeadquarters
1958Year Founded
$55.8MTotal Funding
ANGEL_INDIVIDUALCompany Stage
Fintech, Financial ServicesIndustries
10,001+Employees
Benefits
Health Insurance.
Life Insurance.
Dental Insurance.
Disability Insurance.
Accidental Death & Dismemberment Insurance.
Risks
CBDCs could reduce reliance on Visa's payment network.
'Buy Now, Pay Later' services may decrease traditional credit card transactions.
Fintech startups offering zero-fee transactions could pressure Visa's revenue model.
Differentiation
Visa operates a global digital payment network connecting millions daily.
The company focuses on financial inclusivity and sustainability in its operations.
Visa collaborates with central banks on Central Bank Digital Currencies (CBDCs).
Upsides
Visa's partnership with fintechs enhances cross-border payment solutions.
Adoption of blockchain technology could revolutionize digital payments.
AI-driven fraud detection systems are reducing fraudulent activities.
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