Data Solution Architect Job San Mateo area,California USA,IT/Tech

10+ years of experience in Data Engineering, Data Integration, or Data Platform delivery.
Strong hands-on experience with Databricks on AWS.
Expertise in Apache Spark, PySpark, Delta Lake, and Lakehouse architecture.
Experience designing and implementing enterprise-scale data pipelines.
Strong understanding of AWS services such as S3, Glue, Lambda, Redshift, IAM, and Cloud Watch.
Hands-on experience with MDM implementations and integrations.
Experience with data quality, data governance, lineage, and master data management processes.
Strong experience integrating enterprise systems using middleware platforms such as Mule Soft, Boomi, Kafka, or API-based integrations.
Experience working with structured, semi-structured, and unstructured datasets.
Strong SQL and Python development skills.
Experience with Agile delivery methodologies and Dev Ops practices.
Experience leading distributed teams and managing stakeholder communications.

Secondary Skills (Good to Have)

Experience with Snowflake or Microsoft Fabric.
Exposure to AI/ML enablement using Databricks ML or AWS Sage Maker.
Experience with Unity Catalog and data governance frameworks.
Knowledge of Data Mesh and Data Product concepts.
Experience with real-time streaming using Kafka or Kinesis.
Experience with Informatica IDMC, Talend, or Azure Data Factory.
Knowledge of Healthcare, life sciences, retail, manufacturing, or financial services domains.
Exposure to GenAI and enterprise AI adoption initiatives.

Role Summary

Client is seeking a seasoned Data Engineering Lead with strong expertise in Databricks on AWS, MDM, and enterprise data integration.
The candidate will lead the design and delivery of modern data platforms that enable trusted, governed, and scalable data consumption across business functions.
This role requires deep experience in cloud-based data engineering, middleware integrations, data governance, and enterprise-scale analytics solutions.
The ideal candidate will work closely with business, architecture, analytics, and engineering teams to drive data modernization initiatives.

Why This Role Matters

This role will be instrumental in enabling the client's enterprise data modernization journey.
The individual will help establish a scalable and governed data foundation that supports advanced analytics, AI/ML initiatives, and business decision-making.
Success in this role will directly improve data quality, consistency, and accessibility across critical business domains.

What You'll Do

Lead the design and implementation of Databricks-based data platforms on AWS.
Architect scalable Lakehouse solutions supporting enterprise analytics workloads.
Design and develop complex ETL/ELT pipelines using Databricks, Spark, and cloud-native services.
Drive MDM strategy, implementation, and integration across business applications and data platforms.
Define data integration patterns using APIs, middleware, event-driven architectures, and messaging frameworks.
Collaborate with business stakeholders to understand data requirements and translate them into technical solutions.
Establish data governance, metadata management, and data quality frameworks.
Optimize data processing performance, scalability, and operational monitoring.
Define CI/CD processes and deployment standards for data engineering assets.
Mentor engineering teams and provide technical leadership throughout the project lifecycle.
Support architecture reviews, solution design discussions, and technical decision-making.
Ensure compliance with organizational standards, security requirements, and best practices.

Soft Skills & Other Skills Communication Skills

Ability to communicate complex technical concepts to business and executive stakeholders.
Effective verbal, written, and presentation skills.
Facilitate architecture reviews, workshops, and stakeholder discussions.

Stakeholder Management

Leadership Skills