GG12.1Role Value PropositionData 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 demonstrate Big Data, Engineering and Cloud expertise. Expected to be highly proficent at technical skills.

Besides contributing in individual capacity, this role also leads and develops Data and Analytics talentExperience12-14 years of relevant experienceEducationBachelors degree in computer science, information technology or equivalent educational qualificationResponsibilities
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
Solve complex business problems using data and advanced technologies
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)




Independently lead design, solutioning & estimations
Provide people leadership: coach, develop and engage talent Technical Skills
SQL, Python/Scala
NoSql and distributed databases (Hbase, Cosmos DB)
ETL pipleine design and development; Solutioning and estimation
Big Data Frameworks : Apache Spark, Hadoop, Hive
Cloud platforms: Azure data factory, Eventhub, Azure functions, Synapse, Databricks
Datawarehouses, data marts, data lakes
Medallion architecture, and other design patterns
Performance tuning, optimization, and data quality validation
Real-time and batch data processing , streaming pieplines with Spark
Communication skills, analytical skills, structured problem-solving skills,mentorship & people leadership skills
Storytelling skills , Partner & Stakeholder engagement experience
People leadership: talent development & engagement experienceGood To have
DevOps practices: Git, AzureDevops, CI/CD pipelines
Unix shell scripting, MongoDB, Nifi
Lambda architecture, Kappa Architecture
Exposure to Gen AI technology and tools
Banking Financial Services and Insurance domain knowledge

📌 Lead Big Data Engineer I Pune (India)
🏢 MetLife
📍 India