Data Engineer

About the role

The Data Engineer at GEI is responsible for designing, building, and maintaining data pipelines and integrations that power GEI's AI solutions and digital initiatives. This role ensures enterprise data is accessible, reliable, and governed for AI capabilities.

Responsibilities

• Design, build, and maintain data pipelines that ingest, transform, and deliver enterprise data to AI solutions and business applications.
• Develop and manage integrations across enterprise data sources using APIs, Graph connectors, event-driven architectures, and batch/streaming patterns.
• Build and maintain data stores and indexing infrastructure that support retrieval-augmented generation (RAG) and other AI consumption patterns.
• Implement data quality, validation, and lineage controls to ensure accuracy and trustworthiness of data feeding AI workflows.
• Support and optimize data models underpinning Power BI dashboards and AI-enabled analytics.
• Collaborate with AI Engineers to define data contracts and ensure pipeline outputs meet solution requirements for schema, latency, and freshness.
• Instrument data pipelines for monitoring, alerting, cost control, and performance optimization.
• Implement data governance and security controls including access management, encryption, and compliance with organizational data policies.
• Identify data-related risks and support mitigation strategies in collaboration with architecture and platform teams.

Requirements

• 4+ years of data engineering experience, with demonstrated ability to build and operate production data pipelines.
• Proficiency in Python and SQL; experience with PySpark or Spark is strongly preferred.
• Experience with Azure data services, including Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage, and Azure SQL.
• Familiarity with Azure AI Search, Cosmos DB, or similar services used to support AI retrieval and storage patterns.
• Experience building and managing ETL/ELT pipelines with structured, semi-structured, and unstructured data sources.
• Knowledge of data modeling, schema design, and indexing strategies for both analytical and AI workloads.
• Familiarity with infrastructure-as-code and CI/CD practices for data pipeline deployment (e.g., Terraform, Azure DevOps).
• Knowledge of data governance principles, including data cataloging, lineage, access control, and privacy requirements.
• Knowledge of security best practices for data solutions, including encryption at rest and in transit, role-based access control, and private networking.
• Prior experience in professional services, engineering, or construction environments is a plus.
• Azure or Databricks certifications (e.g., Azure Data Engineer Associate, Azure Solutions Architect Expert, Databricks Data Engineer Professional) are a plus.

Qualifications

• Education: Bachelor's degree in Computer Science, Information Systems, or related field.
• Experience: 4+ years of relevant experience.

Skills

• Data Engineering
• Python
• SQL
• Azure Data Services
• Azure AI Search
• Azure Synapse Analytics
• Azure Data Lake Storage
• Azure SQL
• Elasticsearch
• Apache Spark
• Terraform
• Azure DevOps
• Data Modeling
• Data Governance
• Data Security
• CI/CD
• Infrastructure-as-code
• ETL/ELT
• Power BI
• AI
• Machine Learning
• Cloud Computing
• Big Data
• Database Management
• Software Development
• Collaboration
• Problem Solving
• Technical Writing
• Project Management
• Communication
• Leadership
• Teamwork
• Attention to Detail
• Time Management
• Adaptability
• Continuous Learning
• Customer Service
• Problem Solving
• Technical Proficiency
• Technical Writing
• Technical Support
• Technical Training
• Technical Troubleshooting
• Technical Writing
• Technical Writing
• Technical Writing
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