Senior Big Data Engineer I

Position Summary

MetLife established a Global capability center (MGCC) in India to scale and mature Data & Analytics, technology capabilities in a cost-effective manner and make MetLife future ready. The center is integral to Global Technology and Operations with a with a focus to protect & build MetLife IP, promote reusability and drive experimentation and innovation. The Data & Analytics team in India mirrors the Global D&A team with an objective to drive business value through trusted data, scaled capabilities, and actionable insights. The operating models consists of business aligned data officers- US, Japan and LatAm & Corporate functions enabled by enterprise COEs - data engineering, data governance and data science.

Role Value Proposition 

Senior Big Data Engineer plays a critical role in data and analytics life cycle and significantly contributes to production grade data and analytics solutions. The role requires one to demonstrateBig Data, Engineering and Cloud expertise.It is an individual contributor role, expected to independently function.

Job Responsibilities

•Design, build, and maintain robust ETL/ELT pipelines on cloud(Azure) or on-prem to collect, ingest and store large volumes of structured and unstructured data for batch/real time processing

•Monitor, optimize, and troubleshoot data pipelines to ensure reliability, scalability, and performance

•Ensure data processing, quality, security, and compliance guidelines, policies and standards are followed

•Collaborate with multiple partners from Business, Technology, Operations and D&A capabilities (Data Governance, Data Quality, Data Modeling, Data Architecture, Data science, DevOps, BI & insights)

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Education

Bachelor’s degree in computer science, information technology or equivalent educational qualification

Experience 

(In Years)

8-11+ years of relevant experience

Technical Skills

•SQL, Python/Scala

•NoSQL and distributed databases (Hbase, Cosmos DB)

•ETL pipeline development

•Big Data Frameworks:Apache Spark, Hadoop, Hive

•Cloud platforms: Azure data factory, Eventhub, Azure functions, Synapse, Databricks 

•Datawarehouses, data marts, data lakes

•Medallion architecture

•Performance tuning, optimization, and data quality validation 

•Real-time and batch data processing , streaming pipelines with Spark

•Communication skills, analytical skills, structured problem-solving skills., 

•Partner, Stakeholder engagement experience

Preferred skills

• DevOps practices: Git, AzureDevops, CI/CD pipelines

• Unix shell scripting, MongoDB, Nifi 

• Exposure to Gen AI technology and tools