Data Engineering Manager

Dynatron Software•Richardson, TX

2d•$160,000 - $190,000•Remote

About The Position

Dynatron is transforming the automotive service industry with intelligent SaaS solutions that drive measurable results for thousands of dealerships and service departments. Our proprietary analytics, automation, and AI-powered workflows help service leaders improve profitability, enhance customer satisfaction, and operate with greater efficiency. With strong momentum, expanding product capabilities, and growing market demand, we’re scaling, and we’re just getting started. We’re seeking an experienced and driven Data Engineering Manager to lead and scale Dynatron’s data engineering organization. This is a high-impact leadership role responsible for building, operating, and evolving the data platforms that power analytics, reporting, machine learning, and AI-driven products across the business. You will lead teams across Data Engineering, Data Architecture, and Data Science enablement, owning delivery of reliable, scalable, and cost-efficient data systems that support both current operations and future growth. This is not a maintenance role. It is a delivery, innovation, and execution-focused leadership role for someone who thrives in complexity, values accountability, and consistently delivers outcomes. The ideal candidate brings deep technical credibility, strong people leadership, and the ability to partner effectively with executives, product leaders, and engineers to translate business priorities into durable data solutions.

Requirements

10+ years of hands-on experience in data engineering, with 4+ years leading teams and managing budgets (~$2M annually).
Proven experience designing and operating modern data platforms at scale.
Strong technical foundation across data architecture, pipelines, warehousing, and streaming systems.
Hands-on experience with tools such as Airflow, Fivetran, Kafka, dbt, and modern data warehouses (Snowflake, Databricks, Redshift).
Strong proficiency in Python, SQL, and data modeling techniques.
Experience building and operating data platforms on AWS.
Solid understanding of real-time data processing, ML/AI enablement, and analytics architectures.
Demonstrated ability to lead, inspire, and hold teams accountable.
Strategic mindset with the ability to align data initiatives to business outcomes.
Strong project management and execution discipline.
Clear, confident communicator with technical and non-technical stakeholders.
Ability to challenge ideas constructively and navigate differing viewpoints professionally.
High integrity, strong sense of urgency, and ownership mentality.

Responsibilities

Own and execute Dynatron’s data engineering strategy aligned with business objectives and product roadmaps.
Evaluate and adopt emerging data technologies while balancing performance, scalability, and cost.
Ensure data platforms are built for long-term sustainability, extensibility, and operational excellence.
Lead, mentor, and scale a high-performing data engineering organization.
Set clear expectations, drive accountability, and foster a culture of continuous improvement.
Support career growth through coaching, feedback, and merit-based development opportunities.
Oversee the design, implementation, and operation of data warehouses, data lakes, transactional databases, and pipelines.
Guide development of ETL/ELT workflows, APIs, real-time data streams, and analytics layers.
Partner closely with Data Science and ML teams to enable production-grade AI and ML workflows.
Lead execution of data initiatives, ensuring on-time delivery within scope and budget.
Establish and monitor KPIs, SLAs, and operational metrics across data systems.
Identify opportunities to improve reliability, performance, and developer productivity.
Act as a trusted partner to Product, Engineering, Analytics, and Executive leadership.
Translate complex technical concepts into clear business implications and trade-offs.
Proactively communicate risks, progress, and long-term impact.
Ensure data platforms adhere to security, privacy, and compliance requirements (SOC 2, PCI, GDPR, CCPA, etc.).
Implement governance standards across data quality, access control, lineage, and retention.