Sr. Data Engineer

Dynatron; United States; Remote; USD 140,000 - 210,000; Full time

Benefits offered by this job

Remote-first culture

Equity Incentive Plan

Health, dental, and vision insurance

Disability and life insurance

401(k) with company match

Flexible vacation policy and 11 paid-h

Job summary

Dynatron is seeking a Senior Data Engineer to lead the data team in building robust, production-grade data pipelines powering real-time analytics, AI/ML initiatives, and enterprise reporting. You will work hands-on in AWS and modern cloud data stacks, with Snowflake or Databricks, shaping scalable data ecosystems.

You’ll mentor engineers, collaborate with Product and Analytics, and own data quality, contracts, and observability, while ensuring performance, security, and maintainability across

Qualifications

• 6-8+ years in data engineering for large-scale distributed systems.
• Expert-level Python and PySpark with strong SQL skills.
• Deep hands-on experience with Snowflake or Databricks within an AWS ecosystem.
• Streaming experience with Kinesis or Kafka.
• Automated testing frameworks, data profiling, and pipeline validation.
• Strong docs habit and ownership mindset.
• Nice-to-have: SnowPro Core, Databricks Certified Data Engineer, or AWS Data Engineer.

Responsibilities

• Build and maintain complex data pipelines using AWS Glue, Step Functions, or Databricks Workflows.
• Implement modular data structures with Medallion Architecture and dimensional modeling.
• Manage scalable data storage using AWS S3 as landing zone and data lake foundation.
• Optimize storage formats (Delta, Iceberg, Parquet) and compute for performance and cost.
• Develop real-time ingestion pipelines with AWS Kinesis or Kafka.
• Implement CDC with Debezium or Fivetran for low-latency analytics.
• Own end-to-end data validation and QA in ETL/ELT pipelines.
• Enforce data contracts and schema evolution for data quality.
• Engineer ML-ready datasets and Feature Stores; operationalize ML workflows with Snowflake Cortex, Databricks AI, or AWS Bedrock.
• Mentor junior engineers; collaborate with Product and ML teams to translate designs into code.
• Document playbooks and technical specs; maintain ownership.

Skills

Python

PySpark

SQL

Snowflake

Databricks

AWS

Kinesis

Kafka

Data Modeling

ETL/ELT

Data QC

CDC

Tools

AWS Glue

Databricks Workflows

Job description

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.

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.