Data Engineer

Root It Services; Snowflake (AZ); Hybrid; USD 143,270,000 - 200,579,000; Full time

Job summary

Root It Services is seeking a Data Engineer to help modernize and scale a cloud data platform. You will design and develop scalable data pipelines, build cloud-native data solutions on AWS, transform data using dbt, and enable analytics, reporting, and machine learning workloads across the enterprise.

This remote contract role runs 6–12 months and pays $50–$70/hr. You should have 4+ years of data engineering experience, strong Python, PySpark, SQL, Snowflake, and dbt skills, plus AWS expertise.

Qualifications

• 4+ years of Data Engineering experience
• Strong proficiency in Python, PySpark, SQL, and Apache Spark
• Hands-on experience with Snowflake Data Cloud
• Experience developing and maintaining dbt models and transformations
• Strong knowledge of AWS services such as: AWS S3, AWS Glue, AWS Lambda, AWS EMR, AWS Redshift, AWS IAM, Amazon CloudWatch
• Experience building batch and streaming data pipelines
• Strong understanding of dimensional modeling, star schema, and snowflake schema
• Experience with workflow orchestration tools such as Airflow
• Knowledge of CI/CD pipelines and Git-based version control
• Experience with data quality, testing, and monitoring frameworks
• Excellent communication and stakeholder management skills
• Experience with Kafka or real-time streaming platforms
• Experience with Terraform or Infrastructure as Code (IaC)
• AWS Certification (Data Analytics, Solutions Architect, or Developer)
• Experience supporting analytics and machine learning workloads
• Exposure to Agile/Scrum environments

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines
• Create and optimize data models in Snowflake
• Process large-scale datasets using PySpark and Apache Spark
• Build and maintain AWS-based data solutions
• Implement data quality, monitoring, and governance practices
• Collaborate with analytics, BI, and data science teams
• Optimize data warehouse performance and cost efficiency

Skills

Data Engineering

ETL/ELT pipelines

Cloud data workloads

Stakeholder communication

Tools

Python

PySpark

SQL

Apache Spark

Snowflake

dbt

AWS S3

AWS Glue

AWS Lambda

AWS EMR

AWS Redshift

AWS IAM

Amazon CloudWatch

Airflow

Terraform

Job description

Job Description

Help a leading organization modernize and scale its cloud data platform. You will design and develop scalable data pipelines, build cloud-native data solutions on AWS, transform data using dbt, and enable analytics, reporting, and machine learning workloads across the enterprise.

Remote

6–12 Months

$50–$70/hr

Key Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines
• Create and optimize data models in Snowflake
• Process large-scale datasets using PySpark and Apache Spark
• Build and maintain AWS-based data solutions
• Implement data quality, monitoring, and governance practices
• Collaborate with analytics, BI, and data science teams
• Optimize data warehouse performance and cost efficiency

Requirements

4+ years of Data Engineering experience

Strong proficiency in Python, PySpark, SQL, and Apache Spark

Hands-on experience with Snowflake Data Cloud

Experience developing and maintaining dbt models and transformations

Strong knowledge of AWS services such as:

• AWS S3
• AWS Glue
• AWS Lambda
• AWS EMR
• AWS Redshift
• AWS IAM
• Amazon CloudWatch

Experience building batch and streaming data pipelines

Strong understanding of dimensional modeling, star schema, and snowflake schema

Experience with workflow orchestration tools such as Airflow

Knowledge of CI/CD pipelines and Git-based version control

Experience with data quality, testing, and monitoring frameworks

Excellent communication and stakeholder management skills

Preferred Qualifications

Experience with Kafka or real-time streaming platforms

Experience with Terraform or Infrastructure as Code (IaC)

AWS Certification (Data Analytics, Solutions Architect, or Developer)

Experience supporting analytics and machine learning workloads

Exposure to Agile/Scrum environments