BridgeFlair LLC
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer with a contract length of "X months" and a pay rate of "$X per hour." Required skills include AWS services, Apache Spark, Python, and experience with Databricks. Location: "Remote/On-site."
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
July 14, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Malvern, PA
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🧠 - Skills detailed
#Terraform #AI (Artificial Intelligence) #Code Reviews #AWS Kinesis #SNS (Simple Notification Service) #Spark (Apache Spark) #Security #Cloud #Apache Airflow #Infrastructure as Code (IaC) #Business Analysis #Database Performance #Documentation #S3 (Amazon Simple Storage Service) #EDW (Enterprise Data Warehouse) #Data Pipeline #Data Processing #Python #IAM (Identity and Access Management) #Redshift #SQL (Structured Query Language) #"ETL (Extract #Transform #Load)" #GitHub #SQL Queries #Compliance #Data Engineering #AWS (Amazon Web Services) #Data Science #Airflow #Databricks #Data Quality #Apache Kafka #Data Governance #Lambda (AWS Lambda) #Scala #Apache Spark #PySpark #Data Ingestion #AWS Glue #Data Warehouse #SQS (Simple Queue Service) #AWS S3 (Amazon Simple Storage Service) #Kafka (Apache Kafka) #DevOps #dbt (data build tool)
Role description
Data Engineer – Roles & Responsibilities
Responsibilities
• Design, develop, and maintain scalable data pipelines using AWS Glue, Lambda, S3, Redshift, and Apache Spark.
• Build and optimize ETL/ELT workflows using PySpark, Python, and DBT for large-scale data processing.
• Develop and maintain complex Apache Airflow DAGs and AWS Step Functions for workflow orchestration.
• Create high-performance analytical SQL queries, stored procedures, window functions, and optimize database performance.
• Design and implement real-time data ingestion solutions using Apache Kafka and AWS Kinesis.
• Develop infrastructure using Terraform and AWS CloudFormation following Infrastructure-as-Code (IaC) best practices.
• Build and maintain CI/CD pipelines using GitHub Actions or similar DevOps tools.
• Design scalable data models and support enterprise data warehouse and analytics initiatives.
• Collaborate with Data Scientists, Product Managers, Business Analysts, and Application Development teams.
• Monitor data quality, pipeline reliability, and resolve production issues.
• Optimize Spark jobs for performance, scalability, and cost efficiency.
• Ensure data governance, security, and compliance across AWS environments.
• Utilize AI-assisted development tools such as Claude Code or equivalent to improve engineering productivity.
• Support code reviews, technical documentation, and knowledge sharing across engineering teams.
• Experience with Databricks is highly preferred.
Required Skills
• AWS S3, Glue, Lambda, SQS, SNS, EventBridge, IAM
• Redshift
• Apache Spark, PySpark
• Python
• DBT
• Apache Airflow
• AWS Step Functions
• Kafka
• AWS Kinesis
• Terraform
• AWS CloudFormation
• GitHub Actions
• Analytical SQL
• Databricks (Preferred)
• Claude Code or similar AI coding tools
Data Engineer – Roles & Responsibilities
Responsibilities
• Design, develop, and maintain scalable data pipelines using AWS Glue, Lambda, S3, Redshift, and Apache Spark.
• Build and optimize ETL/ELT workflows using PySpark, Python, and DBT for large-scale data processing.
• Develop and maintain complex Apache Airflow DAGs and AWS Step Functions for workflow orchestration.
• Create high-performance analytical SQL queries, stored procedures, window functions, and optimize database performance.
• Design and implement real-time data ingestion solutions using Apache Kafka and AWS Kinesis.
• Develop infrastructure using Terraform and AWS CloudFormation following Infrastructure-as-Code (IaC) best practices.
• Build and maintain CI/CD pipelines using GitHub Actions or similar DevOps tools.
• Design scalable data models and support enterprise data warehouse and analytics initiatives.
• Collaborate with Data Scientists, Product Managers, Business Analysts, and Application Development teams.
• Monitor data quality, pipeline reliability, and resolve production issues.
• Optimize Spark jobs for performance, scalability, and cost efficiency.
• Ensure data governance, security, and compliance across AWS environments.
• Utilize AI-assisted development tools such as Claude Code or equivalent to improve engineering productivity.
• Support code reviews, technical documentation, and knowledge sharing across engineering teams.
• Experience with Databricks is highly preferred.
Required Skills
• AWS S3, Glue, Lambda, SQS, SNS, EventBridge, IAM
• Redshift
• Apache Spark, PySpark
• Python
• DBT
• Apache Airflow
• AWS Step Functions
• Kafka
• AWS Kinesis
• Terraform
• AWS CloudFormation
• GitHub Actions
• Analytical SQL
• Databricks (Preferred)
• Claude Code or similar AI coding tools
