Senior Cloud Data Engineer

Discover your opportunity with Mitsubishi UFJ Financial Group (MUFG), the 5th largest financial group in the world (as ranked by S&P Global, April 2018) with total assets of over $2.9 trillion (106.2 (JPY) as of March 30, 2018) and 150,000 colleagues in more than 50 countries. In the U.S., we're 13,000 strong, working together to positively impact every customer, organization, and community we serve. We achieve this by delivering on our values, putting people first, fostering long-term relationships built on honesty and mutual understanding, and inspiring the best in each other. This is all part of our inclusive, high-performing culture supported by Total Rewards that include our cash balance pension plan. Join a team that's working to fulfill its vision to be the world's most trusted financial group.

Job Summary

Enterprise Data Platform, Master Data Management and Report Automation Sr. Cloud Data Engineer participates and drives the development lifecycle of the firm's next generation data environment. Development entails design, build and operating the platform. The engineer is an enabler, self-starter and is execution focused with knowledge on technologies such as Kafka, AWS (EC2, S3, IAM, Security Groups, etc), StreamSets Data Collector and Docker / OpenShift. The engineer is an expert operator in DevSecOps methodologies and Agile / Waterfall project delivery in large corporations. The successful engineer embodies the MUFG values of Integrity, Respect, Service, Teamwork, Inclusion and Stewardship.

Major Responsibilities:

  • Develop, maintain, fix and improve deployment pipelines for infrastructure, applications and data via Bitbucket, Jenkins, Nexus and Veracode.
  • Maintain and ensure operational metadata (logs, performance metrics, etc) are available for EDP for diagnostics and trending.
  • Has the ability to write infrastructure, application and data test cases and participate in code review sessions.
  • Upgrade and evolve the technology stack as required.
  • Performance analysis and tuning of infrastructure and data processing.

  • Minimum 10 years engineering in a data environment and 10 years working in a financial institution or similar.
  • Programming experience with Python, Go or Java development environments.
  • Expertise in streaming technologies like Kafka, AWS Kinesis, etc. Understands partitions, topics, producers and consumers.
  • Familiar with big data concepts like map reduce, key value and object file stores.
  • Has worked on Data Warehouse systems and understands the way data is stored like third normal form and ensemble data modeling, transmitted across the enterprise, checked and reconciled.
  • Data engineering know how of SQL, Informatica PowerCenter or similar.
  • Knowledge of a MDM system (Informatica) and a Report Automation platform (Axciom).
  • Awareness of data governance aspects like metadata, business glossaries, canonical models, etc
  • Aptitude for learning in a dynamic environment. Tackling new projects, new teams, new technologies means having to adjust and learn all the time.
  • Positive, can-do attitude.
  • Excellent collaboration skills and a real passion for problem solving, with the ability to work alternative coverage schedules
  • Validated verbal and written communication skills required due to the dynamic nature of collaboration with leadership, partners and other engineering teams.
  • Bachelor's degree in Computer Science, or a related field.

The above statements are intended to describe the general nature and level of the work being performed. They are not intended to be construed as an exhaustive list of all responsibilities, duties, and skills required of personnel so classified .

We are proud to be an Equal Opportunity / Affirmative Action Employer and committed to leveraging the diverse backgrounds, perspectives, and experience of our workforce to create opportunities for our colleagues and our business. We do not discriminate in employment decisions on the basis of any protected category.

A conviction is not an absolute bar to employment. Factors such as the age of the offense, evidence of rehabilitation, seriousness of violation, and job relatedness are considered in all employment decisions. Additionally, it's the bank's policy to only inquire into a candidate's criminal history after an offer has been made. Federal law prohibits banks from employing individuals who have been convicted of, or received a pretrial diversion for, certain offenses.

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