Sr. Data Engineer

About DynatronDynatron 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 OpportunityDynatron 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 forbuilding, optimizing, and maintaining the robust data pipelines that power our real-timeanalytics, AI/ML initiatives, and enterprise reporting. You are a hands-on expert in AWSand modern cloud data stacks, specifically Snowflake or Databricks, and possess theengineering rigor to build scalable, production-grade data ecosystems.What You’ll DoPipeline Development & AWS Data Lake EngineeringBuild 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 & IngestionDevelop 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 pipelinesEnforce 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/AIEngineer 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 & CollaborationMentor 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 QualificationsExperience: 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 skillsPlatforms: 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 & OwnershipStrong communication skills with the ability to explain technical concepts clearly to technical and non-technical stakeholdersCollaborative mindset with the ability to partner effectively across Product, Engineering, Analytics, ML, and leadership teamsHigh standards for quality, maintainability, performance, and operational disciplineStrong ownership mindset with the ability to move quickly, solve problems thoughtfully, What Success Looks LikeThis role rewards data engineers who:Build scalable, reliable, and secure data systems that support real business outcomesOperate with urgency, ownership, and strong engineering disciplineThink beyond individual pipelines to improve platform quality, observability, and long-term maintainabilityHelp Dynatron turn trusted data into smarter products, better decisions, and stronger customer outcomes and follow through reliablyPartner effectively across technical and business teamsCompensation & BenefitsCompetitive base salaryParticipation in Dynatron’s Equity Incentive PlanComprehensive health, dental, and vision insuranceEmployer-paid disability and life insurance401(k) with competitive company matchFlexible vacation policy and 11 paid holidaysRemote-first cultureOngoing professional development opportunitiesWhy DynatronOpportunity to build and scale the data foundation of a growing, AI-enabled SaaS companyHigh-impact role supporting real-time analytics, machine learning, enterprise reporting, and product innovationClose partnership across Data, Product, Engineering, Analytics, and business leadershipValues-driven culture built on accountability, urgency, and delivering measurable resultsRemote-first environment offering flexibility, autonomy, and trust