Dynatron Software

About Dynatron

Dynatron is transforming the automotive service industry with intelligent SaaS solutions that drive measurable results for thousands of dealership service departments. Our proprietary analytics, automation, and AI-powered workflows empower service leaders to improve profitability, elevate customer satisfaction, and operate with greater efficiency. With accelerating growth, expanding product innovation, and increasing market demand, we are scaling quickly and data is a critical driver of what comes next.

The Opportunity

Dynatron is seeking a highly skilled Senior Data Engineer to join our growing data team.
While our architects define the blueprint, you will be the lead craftsman responsible for
building, optimizing, and maintaining the robust data pipelines that power our real-time
analytics, AI/ML initiatives, and enterprise reporting. You are a hands-on expert in AWS
and modern cloud data stacks—specifically Snowflake or Databricks—and possess the
engineering rigor to build scalable, production-grade data ecosystems.

Work Hours & Collaboration Expectations

This role is remote, with required availability during core collaboration hours with U.S.-based teams:

• 9:00 AM – 2:00 PM EST
• 8:00 AM – 1:00 PM CST
• 7:00 AM – 12:00 PM MST
• 6:00 AM – 11:00 AM PST

The remaining three hours of the workday may be completed before or after core hours at your discretion.

What You’ll Do

Pipeline Development & AWS Data Lake Engineering

• Build and maintain complex data pipelines using AWS Glue, Step Functions, or Databricks Workflows.
• Implement modular data structures using advanced modeling techniques such as Medallion Architecture and Dimensional Modeling.
• Manage scalable data storage solutions using AWS S3 as the primary landing zone and data lake foundation.
• Optimize storage formats (Delta, Iceberg, Parquet) and compute performance to ensure high-throughput and cost-effective processing.
• Build decoupled, event-driven architectures using AWS SNS and SQS to handle high-throughput messaging between data services.

• Develop and deploy real-time ingestion pipelines using AWS Kinesis or Kafka.
• Implement Change Data Capture (CDC) via tools like Debezium or Fivetran to support low-latency operational analytics.

• Own end-to-end data validation and QA by building automated data quality checks directly into the ETL/ELT pipelines.
• Enforce strict data contracts and schema evolution guidelines to maintain high data quality and integrity across domains.
• Implement proactive alerting and observability to catch data drift, pipeline anomalies, and quality drops before they impact downstream users.

• Engineer ML-ready datasets and manage Feature Stores to support the Data Science team.
• Operationalize ML workflows, integrating with services like Snowflake Cortex, Databricks AI, or AWS Bedrock.

• Mentor junior engineers in coding best practices, SQL optimization, and Python development.
• Collaborate closely with Product and ML teams to translate architectural designs into functional code.

• Experience: 6–8+ years of experience in data engineering with a focus on large-scale distributed systems.
• Core Languages: Expert-level Python and PySpark with Strong SQL skills.
• Platforms: Deep hands-on experience with Snowflake or Databricks, built natively within an AWS ecosystem.
• Streaming: Proven track record building streaming applications using Kinesis or Kafka.
• Data Validation: Demonstrated experience implementing automated testing frameworks, data profiling, and pipeline validation (owning the QA of your own pipelines).
• Soft Skills: Strong documentation habits (playbooks, technical specs) and an ownership mindset.
• Certifications (Nice-to-Have): Relevant IT professional certifications, such as SnowPro Core, Databricks Certified Data Engineer Professional, or AWS Certified Data Engineer.

Collaboration & Ownership

• Strong communication skills with the ability to explain technical concepts clearly to technical and non-technical stakeholders.
• Collaborative mindset with the ability to partner effectively across Product, Engineering, Analytics, ML, and leadership teams.
• High standards for quality, maintainability, performance, and operational discipline.
• Strong ownership mindset with the ability to move quickly, solve problems thoughtfully, 

What Success Looks Like

This role rewards data engineers who:

• Build scalable, reliable, and secure data systems that support real business outcomes.
• Operate with urgency, ownership, and strong engineering discipline.
• Think beyond individual pipelines to improve platform quality, observability, and long-term maintainability.
• Partner effectively across technical and business teams.

• Help Dynatron turn trusted data into smarter products, better decisions, and stronger customer outcomes.and follow through reliably.

Why Dynatron

• Opportunity to build and scale the data foundation of a growing, AI-enabled SaaS company.
• High-impact role supporting real-time analytics, machine learning, enterprise reporting, and product innovation.
• Close partnership across Data, Product, Engineering, Analytics, and business leadership.
• Values-driven culture built on accountability, urgency, and delivering measurable results.
• Remote-first environment offering flexibility, autonomy, and trust.