Senior Data Engineer

West Monroe•Los Angeles, NY

3d•Hybrid

About The Position

West Monroe is seeking a Senior Data Engineer to join its Data & AI practice. The role designs, builds, and optimizes modern cloud data platforms that enable advanced analytics, AI, and business intelligence. Using technologies such as Databricks, Snowflake, and Microsoft Fabric, the Senior Data Engineer helps clients modernize data ecosystems, build scalable pipelines, and establish trusted data foundations for reporting, machine learning, and generative AI. As a technical leader on project teams, this position contributes to solution design, mentors consultants, and delivers high-quality client outcomes. The role is based in a hybrid work environment, with two days per week in the office, and requires 30–50% travel. West Monroe describes itself as a global business and technology consulting firm that works collaboratively with clients to create measurable value and lasting results.

Requirements

At least 4 years of experience designing and implementing enterprise data engineering solutions.
Hands-on experience with Databricks, Snowflake, and/or Microsoft Fabric in enterprise environments.
Experience building cloud-native data pipelines using Spark, SQL, Python, and modern orchestration tools.
Experience with lakehouse architecture, cloud data warehousing, data modeling, and ELT/ETL development.
Experience integrating enterprise data platforms with analytics, business intelligence, machine learning, or generative AI solutions.
Familiarity with Azure cloud services such as Azure Data Factory, Azure Storage, Azure Synapse, Microsoft Fabric, or comparable cloud technologies.
Experience designing and implementing cloud-native data platforms on AWS, Azure, and/or GCP, and using cloud-native data services alongside Databricks, Snowflake, and Microsoft Fabric for scalable, secure, high-performing analytics solutions.
Understanding of data governance, security, metadata management, and data quality best practices.
Experience with Git, CI/CD pipelines, Infrastructure as Code, and agile software delivery practices.
Strong analytical, problem-solving, and communication skills, with the ability to collaborate across technical and business teams.
Experience integrating AI tools such as ChatGPT into daily workflows to improve productivity and insight generation, with critical thinking to assess accuracy, mitigate bias, and ensure quality.
Ability to travel 30–50%.
Commitment to inclusion and diversity and openness to new ideas and perspectives.
Ability to work permanently in the United States without sponsorship.

Responsibilities

Design, develop, and optimize cloud-native data solutions using Databricks, Snowflake, Microsoft Fabric, and related data technologies.
Architect and deliver cloud data solutions on AWS, Azure, and GCP, integrating cloud-native technologies with Databricks, Snowflake, and Microsoft Fabric for enterprise analytics, AI, and business intelligence.
Build scalable batch and streaming data pipelines for enterprise analytics, operational reporting, and AI-enabled applications.
Design and implement lakehouse and data warehouse architectures that improve data accessibility, governance, and performance.
Collaborate with client stakeholders to gather requirements, translate business needs into technical solutions, and deliver data engineering outcomes.
Develop ELT/ETL processes, data models, orchestration workflows, and data quality frameworks.
Create trusted, scalable data foundations supporting machine learning, generative AI, semantic models, and intelligent automation.
Apply data engineering best practices for security, governance, monitoring, performance optimization, and DevOps.
Mentor junior consultants, review technical deliverables, and contribute to solution quality across engagements.
Use AI tools to accelerate analysis, synthesize complex information, and support data-driven client recommendations, evaluating outputs with sound judgment.
Apply AI technologies, including generative AI, automation tools, and data models, to improve insights, delivery efficiency, and client outcomes.