MetLife
Location
Secunderabad | India
Job description
We are looking for you: high performing data and analytics professional - driven by passion and purpose, to drive quality standards and support end-to-end development of actionable, high-impact data and analytics solutions for MetLife s enterprise functions and lines of businesses across markets. The portfolio of work delivers data driven solutions across key business functions such as customer acquisition and targeting, engagement, retention, and distribution, underwriting, claims service & operations, risk management, investments, audit, and tackling hard, open-ended problems.
This position is responsible for requirements gathering, source systems analyses, architecting and delivering right data models to enable development and deployment of data delivery (extraction,transformation, and loading) jobs. The role will collaborate with Business, Data science, BI & Insights teams to develop robust, stable analytical solutions leveraging the cutting-edge technologies and frameworks.
Job Responsibilities:
Enable better decision making (for businesses & enterprise functions) by providing data architecting, data modeling expertise; responsible and accountable for (data/information) requirement gathering, designing technical solution, enabling user acceptance in the delivery of enterprise grade BI, Data Science, Insights & Visualization solutions.
a. Work closely with the Business, BI/ data science teams to review the requirements and data integrations.
b. Work closely with business and BI/data science teams to translate given business problem into analytical solution with defined outcomes.
c. Convert business requirements to functional/technical requirements with detailed transformation rules for ETL team.
d. Analyze the data in depth using SQL/Hive/other big data exploratory tools against various platforms such as Oracle, SQL Server, and others.
e. Write validation scripts to validate the data loaded by ETL team.
f. Should have skills to understand the right functional domain knowledge of the business to be able to gather right requirements for the design.
g. Capture meta data, data lineage, reference data management and data quality rules as part of data governance.
Develop and institutionalize best practices and re-usable components, contribute to research and experimentation efforts
a. Drive thought leadership in terms of best practices, sharing novel ideas, support in framing standards for enhancing Big Data Engineering/BSA space.
b. Demonstrate thought leadership by cross-skilling and up-skilling skills, contributing to research initiatives, knowledge sharing, experimentation, and innovation.
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