Databricks Lakehouse Architect - AWS & MDM Lead

Falcon Smart IT (FalconSmartIT); San Mateo (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Falcon Smart IT is seeking a seasoned Data Engineering Lead to drive Databricks-based data platforms on AWS with a strong emphasis on MDM and enterprise data integration. You will guide governance, data quality, and scalable analytics solutions across business functions.

The role requires deep cloud-based data engineering experience, middleware integrations, and collaboration with business, architecture, analytics, and engineering teams to modernize data platforms for trusted insights.

Qualifications

• Proven track record delivering enterprise-scale data platforms.
• Strong experience with Databricks on AWS and Lakehouse architecture.
• Expertise in MDM, data governance, lineage, and data quality.
• Experience leading distributed data engineering teams and managing stakeholders.

Responsibilities

• Lead design and delivery of Databricks-based data platforms on AWS.
• Architect scalable Lakehouse solutions for analytics workloads.
• Develop complex ETL/ELT pipelines using Databricks, Spark, and cloud services.
• Drive MDM strategy, implementation, and enterprise integration.
• Define data integration patterns using APIs, middleware, and event-driven architectures.
• Mentor engineers and guide architecture reviews and governance.

Skills

Databricks on AWS

Apache Spark / PySpark

Lakehouse architecture

MDM / data governance

APIs / middleware integrations

SQL & Python development

Leadership of distributed teams

Agile / DevOps practices

Tools

Databricks

MuleSoft / Boomi / Kafka

Kafka / Kinesis

Informatica IDMC / Talend / Azure Data Factory

Snowflake / Microsoft Fabric

Job description

Falcon Smart IT is seeking a seasoned Data Engineering Lead to drive Databricks-based data platforms on AWS with a strong emphasis on MDM and enterprise data integration. You will guide governance, data quality, and scalable analytics solutions across business functions.

The role requires deep cloud-based data engineering experience, middleware integrations, and collaboration with business, architecture, analytics, and engineering teams to modernize data platforms for trusted insights.