Senior Data Engineer

West Monroe•Washington, DC

2d•$119,500 - $161,700•Hybrid

About The Position

West Monroe is seeking a Senior Data Engineer to join our Data & AI practice. In this role, you will design, build, and optimize modern cloud data platforms that enable advanced analytics, AI, and business intelligence. Working with technologies such as Databricks, Snowflake, and Microsoft Fabric, you will help clients modernize their data ecosystems, develop scalable data pipelines, and establish trusted data foundations that power reporting, machine learning, and generative AI solutions. As a technical leader on project teams, you will mentor consultants, contribute to solution design, and deliver high-quality outcomes for our clients.

Requirements

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, including Azure Data Factory, Azure Storage, Azure Synapse, Microsoft Fabric, or comparable cloud technologies.
Experience designing and implementing cloud-native data platforms on AWS, Microsoft Azure, and/or Google Cloud Platform (GCP), with expertise leveraging cloud-native data services alongside Databricks, Snowflake, and Microsoft Fabric to deliver scalable, secure, and 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 effectively across technical and business teams.
Experience integrating AI tools (e.g., ChatGPT) into day-to-day workflows to enhance productivity and insight generation, coupled with strong critical thinking to assess accuracy, mitigate bias, and ensure high-quality outputs.
Ability to travel 30 to 50%.
A 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 modern cloud data solutions using AWS, Azure, and GCP, integrating cloud-native technologies with Databricks, Snowflake, and Microsoft Fabric to support enterprise analytics, AI, and business intelligence initiatives.
Build scalable batch and streaming data pipelines that support enterprise analytics, operational reporting, and AI-enabled applications.
Design and implement modern 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 high-quality data engineering outcomes.
Develop robust ELT/ETL processes, data models, orchestration workflows, and data quality frameworks.
Enable AI and advanced analytics by creating trusted, scalable data foundations that support machine learning, generative AI, semantic models, and intelligent automation initiatives.
Apply data engineering best practices around security, governance, monitoring, performance optimization, and DevOps.
Mentor junior consultants, review technical deliverables, and contribute to solution quality across engagements.
Leverage AI tools to accelerate analysis, synthesize complex information, and support data-driven recommendations for clients, exercising sound judgment in evaluating outputs.
Apply AI technologies (e.g., generative AI, automation tools, data models) to enhance insights, improve delivery efficiency, and elevate the quality of client outcomes.