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You will join IDM (Infrastructure Data Quality) team focused on transformation of data quality management within GTI (Global Technology Infrastructure) and contributing to the strategy and execution of IDaaP (Infrastructure Data as a product) initiatives.
You will get fantastic opportunity on creating innovative solutions that transform data as a service. You'll join an inspiring and curious team of technologists dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality products.
This role will be responsible for:
Implementation of next gen technical architecture for data quality framework, to drive automation, scalability and advanced analytics
Developing Machine Learning algorithms for solving complex business problems while handling large amount of structured and unstructured data
Maintaining enhancing, and implementing bug fixes; proactively monitoring priority items, investigating issues and analyzing solutions, and driving problems through to resolution.
Collaborating with cross functional teams on technical data quality requirements to align to the existing offerings and roadmap
Enforcing good agile practices like story estimation and test-driven development.
Driving continuous improvement and innovation.
Knowledge and Experience Required:
6+ years' experience with data engineering lifecycle implementation from concept through implementation
Strong knowledge and experience in data related activities (data quality, data governance, metadata, data management, data standards, data structures, data aggregation, and business requirement) is required.
Strong SQL and relational database / data warehouse skills, data analysis experience is required
Prior experience on data modelling, hands on experience on data integration/analysis tools (e.g., Alteryx ) ,data visualization tools (e.g., Tableau) is preferred
Hands-on experience in at least one programming languages, preferably Python is required.
Strong modeling skills and ability to build practical machine learning models using advanced algorithms such as Random Forests, SVM, Neural Networks would be highly preferred
Familiarity with algorithms on anomaly detection, pattern analysis or big data frameworks such as Hadoop/Spark is a bonus
Experience with GIT / BitBucket is required
Exposure to modern engineering practices like Unit Test-Driven Development, Acceptance Test-Driven Development, and Continuous Integration is required
Experience in Agile development lifecycle methodology is required
Comfortable dealing with ambiguity and fast changing environment. Ability to lead and drive efforts to completion.
Strong written and verbal communication skills with ability to speak to varying levels across the organization.JPMorgan Chase & Co., one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. In accordance with applicable law, we make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as any mental health or physical disability needs.
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