AWS Developer

Compunnel Inc.

Raleigh, NC, United States

6 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

5 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Airflow Amazon Web Services Amazon Elastic Compute Cloud Apache HTTP Server Big Data Software Quality Databases Continuous Integration Data Architecture Data Governance Data Infrastructure Data Vault Modeling

+23 more

Data Warehousing DevOps Dimensional Modeling Distributed Computing Environment Amazon DynamoDB Identity and Access Management Python (Programming Language) Linux System Administration Performance Tuning SQL Databases Data Streaming Virtualization Technology Scripting Snowflake Apache Spark Cloudformation Pyspark AWS Data Analytics Apache Kafka Data Management Functional Programming Cloudwatch Data Pipelines

Job description

Design, develop, and support enterprise-scale data engineering solutions and scalable data pipelines Build and optimize large-scale data processing solutions using SQL, Python, PySpark, and Spark Develop and maintain modern data platform solutions using Snowflake, dbt, Kafka, Airflow, Iceberg, and Lakehouse architectures Design and implement cloud-native data solutions using AWS services such as EMR, Glue, Athena, Lambda, DynamoDB, EC2, IAM, CloudFormation, and CloudWatch Apply data architecture and modeling principles, including dimensional modeling, Data Vault, data warehousing, data governance, and master data management Design and implement data streaming and distributed data processing solutions Evaluate technical approaches and clearly communicate architecture decisions, implementation strategies, challenges, and outcomes Lead technical initiatives and collaborate with engineering teams to deliver scalable data solutions Gather business requirements and translate complex business needs into technical designs and solutions Collaborate with business stakeholders and technical teams throughout the development lifecycle Apply AI-assisted development tools, automation frameworks, and emerging technologies to improve engineering productivity, software quality, and delivery Continuously evaluate and adopt modern engineering practices and technologies that improve data platform capabilities Support CI/CD and DevOps practices for data engineering solutions Troubleshoot performance, reliability, and data quality issues across data pipelines and platforms

Requirements

5+ years of experience Good to Have Good to Have AWS Solutions Architect AWS Developer AWS Data Analytics

Skills SQL Python PySpark AWS Cloud Services Snowflake dbt Kafka Airflow Apache Iceberg and Lakehouse Architecture Data Pipeline Development Data Streaming and Distributed Data Processing CI/CD and DevOps Linux Administration and Scripting Database Technologies Networking and Virtualization Fundamentals Data Architecture Data Modeling Data Governance Requirements Gathering Stakeholder Management Technical Leadership Technical Documentation Troubleshooting and Root Cause Analysis AI-Assisted Development Agile Delivery Distributed Team Collaboration