Source description
About the role
Job Type Full-time Description Data Pipeline Development: Build and maintain efficient, 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 1012 years of handson experience in data engineering. Strong handson expertise in Snowflake data modelling, performance tuning, CDC, security and governance (Snowpipe, Dynamic Tables, DBT, Streams, RBAC). Handson 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). Good understanding of data governance principles and implementation experience. Familiarity with DevOps practices and CI/CD pipelines for data engineering. Excellent problemsolving, communication and stakeholder management skills. Preferred Qualifications Exposure to big data technologies (Spark, Hadoop). Experience with realtime data streaming (Kafka, Kinesis). Knowledge of data cataloguing tools (Collibra, Alation). Experience of using AI tools (Cloude, Cortex) Prior experience in implementing enterprisescale data modernization initiatives. Job Type Full-time Description Data Pipeline Development: Build and maintain efficient, 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 1012 years of handson experience in data engineering. Strong handson expertise in Snowflake data modelling, performance tuning, CDC, security and governance (Snowpipe, Dynamic Tables, DBT, Streams, RBAC). Handson 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). Good understanding of data governance principles and implementation experience. Familiarity with DevOps practices and CI/CD pipelines for data engineering. Excellent problemsolving, communication and stakeholder management skills. Preferred Qualifications Exposure to big data technologies (Spark, Hadoop). Experience with realtime data streaming (Kafka, Kinesis). Knowledge of data cataloguing tools (Collibra, Alation). Experience of using AI tools (Cloude, Cortex) Prior experience in implementing enterprisescale data modernization initiatives.
More at Otelier