Job description

Key Responsibilities • Design, develop, and maintain robust and scalable data pipelines for data ingestion, transformation, and loading. • Build and optimize data models and transformation workflows using DBT . • Develop and support ETL/ELT processes using enterprise ETL tools. • Implement and manage data warehousing solutions using Snowflake . • Utilize AWS S3 for data storage and data lake architectures. • Design and develop serverless data processing solutions using AWS Lambda . • Create and maintain data integration workflows using AWS Glue . • Work with scheduling and orchestration tools to automate and monitor data pipelines. • Ensure data quality, consistency, security, and governance across the data platform. • Troubleshoot data-related issues and optimize system performance. • Collaborate with data analysts, business stakeholders, architects, and application teams to understand data requirements and deliver effective solutions. • Prepare technical documentation and present solutions to technical and non-technical audiences. Required Skills & Qualifications • Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field. • 5+ years of experience in Data Engineering or related roles. • Hands-on experience with: • Snowflake • DBT (Data Build Tool) • Enterprise ETL/ELT tools • Scheduling/Orchestration tools (such as Airflow, Control-M, Autosys, etc.) • AWS S3 • AWS Glue • AWS Lambda • Strong understanding of SQL and data warehousing concepts. • Experience designing and optimizing large-scale data pipelines. • Knowledge of data modeling, data governance, and data quality best practices. • Strong analytical and problem-solving capabilities. • Excellent verbal and written communication skills. • Strong presentation and stakeholder management skills. Preferred Qualifications • Experience with cloud-based data platforms and modern data architectures. • Knowledge of data lake and data warehouse integration patterns. • Experience with CI/CD and DevOps practices for data engineering. • AWS or Snowflake certifications are a plus. • Experience working in Agile environments. Salary: $70,000 - $110,000

Requirements

Must-haves

Design, develop, and maintain robust and scalable data pipelines for data ingestion, transformation, and loading.
Build and optimize data models and transformation workflows using DBT .
Develop and support ETL/ELT processes using enterprise ETL tools.
Implement and manage data warehousing solutions using Snowflake .
Utilize AWS S3 for data storage and data lake architectures.
Design and develop serverless data processing solutions using AWS Lambda .
Create and maintain data integration workflows using AWS Glue .
Work with scheduling and orchestration tools to automate and monitor data pipelines.
Ensure data quality, consistency, security, and governance across the data platform.
Troubleshoot data-related issues and optimize system performance.
Collaborate with data analysts, business stakeholders, architects, and application teams to understand data requirements and deliver effective solutions.
Prepare technical documentation and present solutions to technical and non-technical audiences.
Required Skills & Qualifications
Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
5+ years of experience in Data Engineering or related roles.
DBT (Data Build Tool)
Enterprise ETL/ELT tools
Scheduling/Orchestration tools (such as Airflow, Control-M, Autosys, etc.)

Nice-to-haves

Experience with cloud-based data platforms and modern data architectures.
Knowledge of data lake and data warehouse integration patterns.
Experience with CI/CD and DevOps practices for data engineering.
AWS or Snowflake certifications are a plus.
Experience working in Agile environments.
Salary: $70,000 - $110,000

Application prep

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Keywords to mirror

awssqlci/cdagile

Platform: careers-page
Effort: medium