Data Engineering Technical Lead - VP

About the role

Axos Bank is seeking a hands-on VP, Data Engineering Technical Lead to help shape, modernize, and scale our enterprise data platform. This role is central to our mission of transforming legacy data systems into a modern, cloud-native Lakehouse environment that powers analytics, AI, and business intelligence across the organization.

Responsibilities

• Modernize legacy ETL pipelines: Lead the transformation of SSIS/SSRS workloads into modular, high-performance pipelines using Databricks, dbt, Fivetran, and Airflow.
• Architect reusable data design patterns: Define and implement standardized frameworks for ingestion, transformation, curation, and consumption layers across the Lakehouse.
• Develop and lead POCs/POVs: Experiment with new technologies (e.g., Delta Live Tables, Iceberg, streaming ingestion, AI-driven observability) to validate architecture choices and influence the enterprise roadmap.
• Leverage AI to accelerate engineering: Use AI-enabled tools like Databricks Assistant, Cursor AI, GitHub Copilot, and dbt Mesh AI tests for code generation, automated testing, documentation, and pipeline optimization.
• Apply ML for operational intelligence: Integrate predictive models to detect pipeline anomalies, data drift, and optimize compute and scheduling.
• Enforce engineering excellence: Drive CI/CD, version control, peer reviews, and observability practices across the data platform.
• Collaborate cross-functionally: Partner with data architects, platform engineers, analysts, and business product owners to translate business needs into technical solutions.
• Mentor data engineers: Provide technical guidance, foster continuous learning, and help the team adopt modern data engineering best practices.
• Optimize performance and cost: Continuously tune Spark workloads, storage tiers, and orchestration logic across Azure and GCP environments.

Requirements

• Bachelor's degree
• 8+ years of experience in data engineering or related technical fields, with at least 3+ years in a lead or senior role.
• Proven experience designing and implementing data design patterns (e.g., CDC, SCD, Medallion, Data Vault, streaming, and batch patterns).
• Deep expertise with Databricks, Apache Spark, dbt, Fivetran, Census, Airflow, and Kafka.
• Solid experience across Azure and/or GCP (e.g., Synapse, Data Factory, BigQuery, Pub/Sub).
• Hands-on experience modernizing legacy ETL (SSIS/SSRS) workloads into cloud-native pipelines.
• Demonstrated ability to build POCs and POVs that validate new tools, frameworks, or architectures.
• Working knowledge of AI-assisted engineering tools for development, observability, or optimization.
• Proficiency in SQL and one programming language (Python, Scala, or Java).
• Strong problem-solving, architectural thinking, and collaboration skills.
• Excellent communicator with the ability to translate technical topics to business stakeholders.