POSITION OVERVIEW:

We are seeking a highly skilled Databricks Architect with deep expertise in data engineering, cloud platforms, and advanced analytics. This individual will play a critical role in designing, implementing, and optimizing scalable data solutions using the Databricks platform. As a trusted technical leader, you will collaborate across engineering, data science, and business teams to architect secure, high-performing, and cost-effective data pipelines and AI-driven systems.

REQUIRED QUALIFICATIONS:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• 8+ years of experience in data engineering or architecture roles
• 3+ years of experience architecting solutions with Databricks
• Expertise with Apache Spark, Delta Lake, MLflow, and Lakehouse architecture
• Proficiency in SQL, Python, and/or Scala
• Hands-on experience with cloud platforms (AWS, Azure, or GCP)
• Deep understanding of distributed computing, performance tuning, and data security
• Experience with CI/CD pipelines, DevOps tools, and orchestration tools (e.g., Airflow, Data Factory)
• Strong communication, leadership, and stakeholder management skills
• Ability to obtain and maintain a Public Trust clearance

PREFERRED QUALIFICATIONS:

• Databricks Certified Data Engineer / Solutions Architect
• Familiarity with MLOps and deploying machine learning models in production
• Experience with data mesh, data fabric, or similar enterprise data architectures
• Public sector, healthcare, or financial services experience is a plus

NOT INTERESTED? REFER A FRIEND!

If this opportunity isn’t the right fit for you, we’d still love your help. Know someone who’d be a great fit for this role? Refer them to us! We’re always looking for talented professionals to join our team

• Architect and implement scalable data platforms and pipelines using Databricks (Spark, Delta Lake, MLflow, etc.)
• Collaborate with stakeholders to design modern data architectures aligned with business needs.
• Lead migration efforts from legacy platforms to Databricks Lakehouse architecture
• Define data governance, quality, and security standards in collaboration with InfoSec and Compliance teams.
• Design and optimize data ingestion, transformation, and consumption patterns
• Guide and mentor data engineers and developers on Databricks best practices
• Integrate Databricks with tools like Power BI, Tableau, Kafka, Snowflake, or other enterprise systems.
• Evaluate and implement machine learning/AI workflows in a Databricks environment.
• Partner with cloud architects to ensure robust infrastructure setup on AWS, Azure, or GCP
• Create technical documentation, architectural diagrams, and data models.