Data Engineer

Aderant • Atlanta, GA

1d

About The Position

We are seeking a Data Engineer to contribute to the development and optimization of our cloud-native data platform. You will be responsible for implementing scalable ETL pipelines, supporting data infrastructure initiatives, and working with modern data lakehouse architectures. This is a hands-on role requiring strong technical skills in AWS data services, distributed computing, and data engineering best practices.

Requirements

• Experience 1-3 years of experience in data engineering or related roles
• Experience building or contributing to data pipelines in production or project environments
• Exposure to cloud data platforms (AWS preferred)
• AWS Data Engineering Skills Practical experience or strong foundational knowledge of AWS data services including:
• Data Processing: AWS Glue, Lambda, Step Functions
• Data Storage: S3, basic familiarity with DynamoDB and/or Redshift
• Data Movement: SQS, basic ETL patterns
• Technical Skills Strong proficiency in Python and SQL (Spark SQL, T-SQL, or similar)
• Solid experience with Apache Spark (PySpark) for data processing
• Working knowledge of data lake table formats (Apache Iceberg, Delta Lake, or Apache Hudi)
• Understanding of dimensional modeling and data warehouse concepts
• Experience with version control (Git) and CI/CD concepts
• Familiarity with data serialization formats (Parquet, Avro, JSON)
• Core Competencies Ability to write clean, maintainable, and well-documented code
• Strong problem-solving skills and attention to detail
• Effective communication skills and ability to work collaboratively
• Self-motivated with ability to work independently when needed
• Eager to learn new technologies and best practices in data engineering

Nice To Haves

• Experience with medallion architectures or tiered data processing patterns
• Familiarity with infrastructure as code tools (Terraform, CloudFormation)
• Understanding of CDC (Change Data Capture) patterns
• Knowledge of data validation libraries (Pydantic, Great Expectations)
• Experience with observability tools (CloudWatch, OpenTelemetry)
• Exposure to data governance and metadata management concepts
• Background in legal, financial, or enterprise SaaS domains
• Experience with async Python (asyncio, aioboto3)

Responsibilities

• Data Pipeline Development
• Build and maintain production-grade ETL pipelines using AWS Glue, PySpark, and Apache Iceberg
• Implement data transformations within our medallion-based data lakehouse (Bronze/Silver/Gold tiers)
• Develop data models following dimensional modeling patterns (Fact/Dimension tables)
• Write efficient, maintainable Python and SQL code for data processing
• Support data quality checks and validation processes
• Hands-On Engineering
• Develop reusable Python modules and utilities for data processing tasks
• Implement event-driven data workflows using Step Functions, Lambda, and SQS
• Optimize Spark jobs for performance and cost efficiency under guidance from senior engineers
• Work with data serialization formats (Parquet, Avro, JSON) for efficient storage and processing
• Participate in code reviews and incorporate feedback to improve code quality
• Collaboration & Growth
• Work closely with senior engineers to implement architectural designs and technical solutions
• Partner with Data Scientists and Analytics Engineers to understand and fulfill data requirements
• Collaborate with Platform and DevOps teams on deployment and monitoring
• Contribute to documentation and knowledge sharing within the team
• Continuously learn and adopt best practices in data engineering

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed