Sr. Data Engineer

Posted 13 Hours Ago
Richardson, TX, USA
In-Office
Senior level
Analytics
The Role
Build and maintain scalable AWS data pipelines, lakehouse storage, real-time streaming and CDC ingestion systems. Own automated data quality, validation, observability, schema evolution, and QA. Engineer ML-ready datasets and feature stores, operationalize AI workflows, optimize distributed processing, and mentor junior engineers while collaborating with Product, ML, Analytics, and Engineering teams.
Summary Generated by Built In
About Dynatron

"Dynatron is championing a new standard of Fixed Ops excellence for automotive dealerships. We cut through the Fixed Ops data fog and deliver unique insights and actions that help you drive revenue growth, expand margins, and uplift retention—all through a unique AI-powered Fixed Ops Data Intelligence Platform, proven methodology, and expert coaching.

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.

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.
Real-Time Data Streaming & Ingestion
  • 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.
Core Data Quality & Automated Validation (QA Ownership)
  • 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.
Engineering for ML/AI
  • 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.
Technical Leadership & Collaboration
  • 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.
Required Qualifications
  • 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.
  • Help Dynatron turn trusted data into smarter products, better decisions, and stronger customer outcomes and follow through reliably.
  • Partner effectively across technical and business teams.
Compensation & Benefits
  • Competitive base salary
  • Participation in Dynatron’s Equity Incentive Plan
  • Comprehensive health, dental, and vision insurance
  • Employer-paid disability and life insurance
  • 401(k) with competitive company match
  • Flexible vacation policy and 11 paid holidays
  • Remote-first culture
  • Ongoing professional development opportunities
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.

Skills Required

  • 6-8+ years of experience in data engineering focused on large-scale distributed systems
  • Expert-level Python experience
  • Expert-level PySpark experience
  • Strong SQL skills
  • Deep hands-on experience with Snowflake or Databricks within an AWS ecosystem
  • Experience building streaming applications using Kinesis or Kafka
  • Experience implementing automated testing frameworks, data profiling, and pipeline validation
  • Strong documentation skills, including playbooks and technical specifications
  • Strong communication and cross-functional collaboration skills
  • Ownership mindset and operational discipline
  • SnowPro Core certification
  • Databricks Certified Data Engineer Professional certification
  • AWS Certified Data Engineer certification
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The Company
HQ: Richardson, TX
121 Employees
Year Founded: 1997

What We Do

At Dynatron Software, we help automotive service departments increase revenue and profitability with our suite of automotive fixed operations data analytics software, comparative insights, and expert coaching. Chaired by industry luminary Les Silver, Dynatron Software has over 24+ years of experience building solutions focused on improving revenue and increasing profitability. Dynatron currently has 175 employees located across the United States! ➤Our Company Mission We strive to be a people-first company where employees enjoy coming to work, the people they work with, and are given the autonomy to succeed. Our company culture is built on a foundation of teamwork, accountability, integrity, clear communication, and positive attitudes. Our experienced executive team leads by example, creating a positive work environment where feedback is straightforward and your hard work is rewarded. This approach has led Dynatron to consistent and steady growth across multiple areas year over year.

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