Job Requirements experience in building and managing ETL/ELT pipelines and data architectures; Experience with relational and NoSQL databases; Experience with big data and distributed processing frameworks;

Experience with cloud data platforms (Azure, AWS, GCP); Exposure to real-time data streaming and event-driven architectures; Experience supporting AI/ML pipelines and analytics workloads is highly preferred; Experience in regulated industries such as banking or financial services is an advantage.

Required Skills Data Engineering, ETL/ELT, Data Modeling, SQL, Python/Scala, Big Data Processing

(Spark/Hadoop), Data Warehousing, Data Lakes, Streaming (Kafka/Event Hubs), Cloud Data

Platforms, Data Governance, Data Quality, Performance Optimization, Problem Solving,

Debugging, Integration Design, Collaboration





Preferred Tools And Certifications Data Platforms: Azure Data Factory, AWS Glue, Google Dataflow, Databricks

Processing: Apache Spark, Hadoop

Databases: SQL Server, PostgreSQL, MySQL, Snowflake, BigQuery

Streaming: Kafka, Azure Event Hubs; Programming: Python, Scala

Orchestration: Airflow; Version Control: Git; DevOps: CI/CD tools (Azure DevOps, Jenkins)

Monitoring: Azure Monitor, Prometheus, Grafana

Certifications: Azure Data Engineer Associate, AWS Data Analytics, Google Skilled Data

Engineer, Databricks certification (preferred)

Work Experience 10-15Years

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