Solutions Architect - Databricks

We are seeking a highly skilled and hands-on Data Architect / Lead Data Engineer to design, build, and scale modern data platforms leveraging the Databricks Lakehouse architecture. This role combines deep technical expertise, architecture design, and client-facing leadership, with an emphasis on driving Databricks adoption across enterprise data ecosystems.

Job Functions:

• Partner with client stakeholders to define data platform strategy and establish Databricks Lakehouse architecture as the standard.
• Design scalable, secure, and high-performance data architectures across batch and real-time processing.
• Build and present reference architectures, solution blueprints, and demos to drive adoption and technical buy-in.
• Translate complex business requirements into robust technical data solutions.
• Design and implement scalable data pipelines using Databricks (PySpark, SQL).
• Build and optimize data models, data marts, and medallion architecture layers (Bronze/Silver/Gold).
• Develop and manage ETL/ELT pipelines, including CDC, incremental processing, and performance tuning.
• Ensure data quality, observability, monitoring, and alerting across production workloads.
• Work with large-scale distributed data systems (Spark, Kafka, etc.).
• Lead and mentor a team of data engineers across multiple workstreams.
• Conduct code reviews, enforce best practices, and create reusable frameworks/patterns.
• Drive end-to-end solution delivery, including architecture, development, and production deployment.
• Collaborate with cross-functional teams including analytics, BI, data science, and cloud engineering.
• Manage stakeholder communication and provide technical thought leadership.

Expected work split:

• 50% Hands-on Technical: Data engineering, pipeline development, architecture implementation
• 50% Leadership & Client Engagement: Solution design, stakeholder management, team leadership

Qualifications (Required):

• 6-10 years of experience in data engineering, data architecture, or analytics engineering
• Strong experience with Databricks ecosystem (Spark, SQL, PySpark, workflows)
• Expertise in:
  • Big Data Technologies (Spark, Kafka, distributed systems)
  • Data Warehousing & Modeling (OLTP/OLAP, dimensional modeling, medallion architecture)
  • ETL/ELT pipeline development and orchestration (Airflow or similar tools)
• Advanced proficiency in SQL and Python (Scala/Java/R is a plus)
• Experience designing and delivering enterprise-grade data architectures
• Strong understanding of performance tuning, query optimization, and large-scale data processing
• Proven ability to translate business needs into scalable technical solutions
• Experience leading teams and working in client-facing environments
• Excellent communication, problem-solving, and stakeholder management skills