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Job Type Full-time Description Data Pipeline Development : Build and maintain effective, scalable, and reliable pipelines to support analytics and reporting workloads. Data Integration: Implement seamless integration across diverse data sources (structured, semi-structured, and unstructured) into Snowflake and AWS-based data platforms (Postgres/Aurora Postgres/Dynamo DB) Data Governance: Establish and enforce data governance frameworks including metadata management, data lineage, data quality and data entitlement. Cloud Engineering: Leverage AWS services (e.g., S3, Glue, Lambda, Redshift, EMR) to design cloud-native data solutions. Performance Optimization: Monitor, troubleshoot and optimize data workflows for speed, scalability, and cost efficiency. Collaboration: Partner with data architects, analysts and business stakeholders to translate requirements into technical solutions. Best Practices : Drive adoption of engineering best practices, including CI/CD, automation, and Infrastructure-as-Code for data platforms. Leveraging AI : Any experience in leveraging AI in execution or implementation of data pipelines and data integration solutions. Requirements 10 12 years of hands-on experience in data engineering. Strong hands-on expertise in Snowflake data modelling, performance tuning, CDC, security and governance (Snowpipe, Agile Tables, DBT, Streams, RBAC). Hands-on experience with AWS services (S3, Glue, Postgres, Redshift, EMR, IAM). Proficiency in RDBMS concepts, SQL and programming languages such as Python or Scala. Experience with ETL/ELT frameworks and workflow orchestration tools ( Snaplogic, Airflow, DBT). Positive understanding of data governance principles and implementation experience Familiarity with DevOps practices and CI/CD pipelines for data engineering. Excellent problem-solving, communication and stakeholder management skills. Preferred Qualifications Exposure to big data technologies (Spark, Hadoop). Experience with real-time data streaming (Kafka, Kinesis). Knowledge of data cataloging tools (Collibra, Alation). Experience of using AI tools (Cloude, Cortex) Prior experience in implementing enterprise-scale data modernization initiatives. Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying. Job Type Full-time Description Data Pipeline Development : Build and maintain effective, scalable, and reliable pipelines to support analytics and reporting workloads. Data Integration: Implement seamless integration across diverse data sources (structured, semi-structured, and unstructured) into Snowflake and AWS-based data platforms (Postgres/Aurora Postgres/Dynamo DB) Data Governance: Establish and enforce data governance frameworks including metadata management, data lineage, data quality and data entitlement. Cloud Engineering: Leverage AWS services (e.g., S3, Glue, Lambda, Redshift, EMR) to design cloud-native data solutions. Performance Optimization: Monitor, troubleshoot and optimize data workflows for speed, scalability, and cost efficiency. Collaboration: Partner with data architects, analysts and business stakeholders to translate requirements into technical solutions. Best Practices : Drive adoption of engineering best practices, including CI/CD, automation, and Infrastructure-as-Code for data platforms. Leveraging AI : Any experience in leveraging AI in execution or implementation of data pipelines and data integration solutions. Requirements 10 12 years of hands-on experience in data engineering. Strong hands-on expertise in Snowflake data modelling, performance tuning, CDC, security and governance (Snowpipe, Agile Tables, DBT, Streams, RBAC). Hands-on experience with AWS services (S3, Glue, Postgres, Redshift, EMR, IAM). Proficiency in RDBMS concepts, SQL and programming languages such as Python or Scala. Experience with ETL/ELT frameworks and workflow orchestration tools ( Snaplogic, Airflow, DBT). Positive understanding of data governance principles and implementation experience Familiarity with DevOps practices and CI/CD pipelines for data engineering. Excellent problem-solving, communication and stakeholder management skills. Preferred Qualifications Exposure to big data technologies (Spark, Hadoop). Experience with real-time data streaming (Kafka, Kinesis). Knowledge of data cataloging tools (Collibra, Alation). Experience of using AI tools (Cloude, Cortex) Prior experience in implementing enterprise-scale data modernization initiatives. Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on
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