Director, Data Engineering

AmeriLife•Washington, NV

2d•$195,000 - $210,000

About The Position

We are seeking an accomplished Director of Data Engineering to lead AmeriLife's enterprise Data Engineering organization and serve as the technical leader for our Databricks platform. This individual will lead the design, development, modernization, and operation of enterprise-scale data platforms while driving engineering excellence across our cloud-native data ecosystem. You will be responsible for establishing engineering standards, mentoring high-performing teams, modernizing legacy platforms, and delivering trusted, scalable data products that power analytics, machine learning, and AI across the enterprise. This role requires a hands-on technology leader who combines deep Databricks expertise with strong leadership, architectural thinking, and a passion for building modern engineering organizations.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.
10+ years of enterprise Data Engineering experience.
5+ years leading Data Engineering teams within large enterprise environments.
Expert-level experience designing and implementing enterprise solutions using the Databricks Lakehouse Platform.
Deep expertise in Apache Spark, Delta Lake, SQL, Python, PySpark, and distributed data processing.
Strong knowledge of Unity Catalog, Delta Live Tables (Lakeflow), Databricks Workflows, notebooks, cluster optimization, and platform administration.
Proven experience modernizing enterprise data platforms and migrating legacy ETL solutions.
Experience implementing CI/CD pipelines, Git-based development, Infrastructure as Code, and DevOps practices.
Strong understanding of Medallion Architecture, dimensional modeling, and modern enterprise data architectures.
Excellent communication, leadership, and stakeholder management skills.

Nice To Haves

Master's degree preferred.
Databricks Certified Data Engineer Professional certification.
Experience with Azure cloud services and enterprise security.
Experience with Data Vault 2.0.
Knowledge of AI, machine learning, and enterprise analytics platforms.
Insurance or Financial Services industry experience.
Experience leading enterprise-scale modernization initiatives.

Responsibilities

Build, lead, and mentor a high-performing Data Engineering organization.
Establish engineering standards, development practices, coding standards, and delivery frameworks.
Foster a culture of ownership, innovation, automation, and continuous improvement.
Develop future engineering leaders through coaching and mentorship.
Serve as the organization's technical leader and subject matter expert for the Databricks Lakehouse Platform.
Define enterprise standards and best practices for Databricks development.
Lead adoption of new Databricks capabilities including Unity Catalog, Lakeflow, Delta Lake, AI/ML, Workflows, and performance optimization.
Establish reusable engineering frameworks that accelerate delivery across the organization.
Partner with Databricks product teams and strategic partners to continuously evolve the platform.
Lead migration of legacy ETL workloads to modern Databricks ELT architectures.
Design scalable, resilient, and cloud-native data pipelines.
Drive automation across ingestion, transformation, orchestration, deployment, monitoring, and recovery.
Optimize performance, scalability, reliability, and cloud cost management.
Build reliable data pipelines supporting analytics, AI, operational reporting, and executive decision making.
Collaborate with Data Architecture to implement enterprise information models and integration patterns.
Partner with Data Governance & Trust to embed governance, metadata, lineage, and quality into engineering solutions.
Enable reusable enterprise data products that reduce duplication and accelerate delivery.
Establish CI/CD, automated testing, infrastructure as code, and DevOps best practices.
Improve platform observability, monitoring, and operational excellence.
Lead root cause analysis and continuous improvement initiatives.
Define engineering KPIs and continuously improve delivery performance.