Aderant is a global industry leading software company providing comprehensive business management solutions for law firms and other professional services organizations with a mission to help them run a better business. We are motivated by a collective desire to drive the legal industry to the forefront of innovation. With over 2,500 clients around the world, including 95 of the top AmLaw 100 firms, we are changing the outside perception of the legal sphere; where there was once resistance to modernization, we are creating a culture that embraces new ideas and technology.

At Aderant, the “A” is more than just a letter. It is a representation of how we fulfill our foundational purpose, serving our clients. It embodies our core values and reminds us that to achieve success, every day must start with the “A”. We bring the “A” to life by fostering a culture of innovation, collaboration, and personal growth. We encourage our diverse teams to bring their whole selves to work – ideas, experience, and passion – to drive our mission forward.

Our people are our strength.

Principal Data Engineer

About the Role

We are seeking a Principal Data Engineer to lead the design, development, and optimization of our cloud-native data platform. You will be responsible for architecting scalable ETL pipelines, mentoring engineers, and driving technical decisions that shape our data infrastructure. This is a hands-on leadership role requiring deep expertise in AWS data services, distributed computing, and modern data lakehouse architectures.

Responsibilities

Technical Leadership

• Architect and evolve our medallion-based data lakehouse (Bronze/Silver/Gold tiers) on AWS

• Design and implement data transformation pipelines that scale to handle petabytes of data

• Establish best practices for data modeling, including dimensional modeling (Fact/Dimension tables) and slowly changing dimensions

• Define and enforce data quality, governance, and security standards across the platform

• Lead technical design reviews and provide guidance on complex engineering challenges

Hands-On Engineering

• Build and maintain production-grade ETL pipelines using AWS Glue, PySpark, and Apache Iceberg

• Develop reusable Python libraries and frameworks for data processing and transformation

• Implement data lineage tracking and query optimization strategies

• Design event-driven data architectures using Step Functions, Lambda, and SQS

• Optimize Spark jobs for performance, cost efficiency, and reliability

Collaboration & Mentorship

• Mentor and coach data engineers, fostering a culture of technical excellence

• Partner with Data Scientists, Analytics Engineers, and Product teams to understand data requirements

• Collaborate with Platform and DevOps teams on CI/CD, observability, and infrastructure automation

• Contribute to architectural decisions and technical roadmap planning

Required Qualifications

Experience

• 8+ years of experience in data engineering, with 3+ years in a senior or lead capacity

• Proven track record of designing and operating large-scale data platforms in production

• Experience leading technical projects and mentoring engineers

AWS Data Engineering Expertise

Proficiency with AWS data engineering services including but not limited to:

• Data Movement & Integration: DMS (Database Migration Service), SQS, Lambda

• Data Processing: AWS Glue, EMR, Step Functions

• Data Storage: S3, DynamoDB, Redshift

• Governance & Observability: DataZone, CloudWatch, CloudTrail

Technical Skills

• Expert-level proficiency in Python and SQL (Spark SQL, T-SQL, or similar)

• Deep experience with Apache Spark (PySpark) for distributed data processing

• Strong knowledge of data lake table formats: Apache Iceberg, Delta Lake, or Apache Hudi

• Proficiency with dimensional modeling and data warehouse design patterns

• Experience with infrastructure as code and CI/CD pipelines (GitHub Actions, Terraform, or CloudFormation)

• Familiarity with data serialization formats (Parquet, Avro, JSON)

Architecture & Design

• Experience designing medallion architectures or similar tiered data processing patterns

• Understanding of CDC (Change Data Capture) patterns and event-driven architectures

• Knowledge of data lineage, cataloging, and metadata management

• Experience implementing row-level security and data access controls

Preferred Qualifications

• Experience with observability frameworks such as OpenTelemetry

• Familiarity with data validation libraries (Pydantic, Great Expectations)

• Experience with async Python (asyncio, aioboto3) for high-throughput applications

• Knowledge of Kubernetes and containerized workloads

• Experience with data mesh or data product architectures

• Background in legal, financial, or enterprise SaaS domains

Technical Environment

You will work with:

• Languages: Python, SQL, Spark SQL

• Compute: AWS Glue 5.0, Lambda, Step Functions

• Storage: S3, DynamoDB, Redshift Serverless

• Formats: Apache Iceberg, Parquet, JSON

• Orchestration: AWS Step Functions, EventBridge

• CI/CD: GitHub Actions, multi-environment deployments

• Observability: CloudWatch, OpenTelemetry, custom metrics pipelines