Data Architect

West Monroe•Los Angeles, NY

19d•Hybrid

About The Position

As a Data Architect at West Monroe, you will lead the design, implementation, and optimization of data solutions that help clients make data-driven decisions. The role shapes data strategies, mentors teams, and ensures the delivery of scalable and efficient data systems. It calls for deep technical expertise, strategic thinking, and leadership to drive innovation and business impact. This is a hybrid role, with two days per week in an office. Employees may align to any West Monroe office in the United States. The role requires 30–50% travel to client sites. The salary range varies by location: Seattle or Washington, D.C., $159,800–$188,000; Los Angeles, $167,400–$196,900; New York City or San Francisco, $175,000–$205,900; and locations not listed above, $152,200–$179,000. Individual salaries are determined based on factors including geography, experience, skills, education, and internal equity. West Monroe describes itself as a global business and technology consulting firm focused on creating measurable client value through collaboration and practical solutions. It is an equal opportunity employer and provides reasonable accommodations during recruiting upon request.

Requirements

Bachelor’s or master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.
At least 10 years of experience in data engineering or architecture, including at least 3 years in a leadership role.
Proven experience architecting and delivering large-scale data solutions in cloud environments such as AWS, Azure, or GCP.
Expertise in data modeling, ETL processes, and data pipeline development.
Experience with data modeling and engineering in Databricks, including Delta Lake and advanced Spark-based transformations.
Expertise in Snowflake data modeling, including schema design, performance optimization, and capabilities such as time travel and zero-copy cloning.
Proficiency in programming languages such as Python, SQL, Java, or Scala.
Experience with big data technologies such as Hadoop, Spark, or Kafka.
Hands-on experience with cloud-native data services such as AWS Redshift, Azure Synapse, or Google BigQuery.
Strong understanding of relational, NoSQL, and graph database technologies.
Familiarity with containerization and orchestration tools such as Docker and Kubernetes.
Ability to lead and mentor technical teams.
Strong communication and interpersonal skills for collaboration with diverse stakeholders.
Ability to manage competing priorities and deliver results under pressure.
Advanced analytical skills for solving complex technical and business problems.
Ability to translate technical concepts into business value for non-technical stakeholders.
Strong organizational and project management skills.
Ability to travel to client sites 30–50% of the time.
Ability to work in a hybrid arrangement, in an office two days per week.

Nice To Haves

Experience with Databricks and/or Snowflake modern data platforms.
Databricks Professional or Azure Data Engineer Associate certification.

Responsibilities

Collaborate with senior leadership and stakeholders to define data strategies and roadmaps aligned with business objectives.
Architect robust, scalable, and secure data solutions, including data pipelines, warehouses, lakes, and analytics platforms.
Establish and enforce data governance frameworks to ensure data quality, security, and compliance with industry standards and regulations.
Mentor and guide junior engineers and foster continuous learning and innovation.
Lead technical teams in delivering high-quality projects on time and within scope.
Evaluate emerging technologies and recommend tools, platforms, and frameworks to enhance data capabilities.
Work with business analysts, data scientists, and software engineers to integrate data solutions into broader business applications.
Monitor and optimize data systems for performance, scalability, and cost efficiency.
Advise clients and provide thought leadership and technical expertise in solving complex data challenges.
Create and maintain technical documentation, including architecture diagrams, data models, and operational procedures.
Lead troubleshooting of critical data issues, ensuring timely resolution and root cause analysis.
Identify opportunities to apply AI, machine learning, and advanced analytics in data engineering solutions.