Data Engineer

Guidehouse is a consulting firm that focuses on providing innovative solutions to complex challenges. They are seeking a Data Engineer to design and maintain data pipelines, optimize data architectures, and ensure data quality for analytics and reporting.

Responsibilities:

• Design, develop, and maintain robust data pipelines and ETL/ELT processes to ingest, transform, and load data from diverse structured and unstructured sources
• Build and optimize data models and data architectures to support analytics, reporting, and operational use cases
• Implement and maintain CI/CD pipelines for data engineering workflows, including data pipelines and scheduled jobs, using version control and automation tools
• Develop and deploy cloud-based data solutions, leveraging services such as object storage, serverless compute, and messaging/queueing services
• Collaborate with database administrators, analysts, and application teams to integrate data sources, design schemas, and support downstream data consumers
• Ensure data quality, integrity, and reliability through validation, monitoring, logging, and alerting
• Support data migration, integration, and modernization initiatives, including legacy system upgrades and cloud adoption efforts
• Troubleshoot and resolve issues in development and production environments to maintain stable and reliable data pipelines
• Document data flows, pipelines, and technical solutions to support knowledge sharing and compliance requirements
• Stay current with emerging tools, technologies, and best practices in data engineering and cloud platforms

Requirements:

• US Citizenship or a Green Card is required
• A Bachelor's degree is required
• A minimum of 4 years of experience in data engineering, software development, or a closely related role is required
• Proficiency in one or more programming languages commonly used in data engineering (e.g., Python, SQL, R, PySpark, or Java)
• Hands-on experience designing and building data pipelines and ETL/ELT processes leveraging commonly used ETL/ELT tools (e.g., SSIS, Databricks, Azure Data Factory)
• Strong knowledge of relational database design, data warehouses, and/or data lakes using star/snowflake schemas
• Experience working with relational and/or NoSQL databases (e.g., Oracle, Postgres, SQL Server, MongoDB)
• Experience working in a cloud environment (AWS or Azure) supporting data solutions
• Familiarity with version control and CI/CD practices for data workflows (e.g., Git, Jenkins)
• Exposure to containerization technologies (e.g., Docker; Kubernetes is a plus)
• Experience using monitoring and logging tools to support data pipeline reliability (e.g., CloudWatch, Splunk, Kibana, Elasticsearch)
• Ability to work effectively in an Agile development environment
• Strong analytical and troubleshooting skills, and the ability to communicate technical concepts clearly to clients, engineers, and business stakeholders
• Ability to work independently in a fast-paced, client-facing environment
• AWS, Azure, Databricks, or other data engineering–related certifications (Datalake of Exasol)
• Experience with data visualization or analytics tools (e.g., Kibana, Tableau, Power BI)
• Familiarity with security, compliance, and data governance best practices in healthcare
• Exposure to microservices-based architectures or AI/ML-enabled data pipelines
• Prior consulting experience