Data Engineer

Data Engineer (Only W-2 basis)

Location: Birmingham, AL (Onsite, 3 Days a week)

Duration: Long Term

Job Requirements:

• Overall, 8 + years of IT experience with 3+ years of experience in data engineering or related roles, preferably in a cloud environment.
• Proficiency in programming languages such as Python, SQL, and Scala.
• Proficiency with Power BI for data visualization and reporting.
• Experience with cloud-based data platforms (e.g., Azure Databricks, AWS EMR).
• Strong understanding of data modeling, ETL processes, and data warehousing concepts.
• Familiarity with big data technologies

Key Responsibilities:

Data Pipeline Development:

• Assist TO in designing, building, and maintaining scalable data pipelines to ingest, transform, and store large volumes of AMI data from various sources.
• Collaborate with data scientists to ensure seamless integration of data pipelines with analytical models and AI processes.

Data Modeling and Architecture:

• Develop and implement data models and architectures optimized for AMI data analytics,

ensuring efficiency, scalability, and data integrity.

• Implement best practices for data storage, partitioning, and indexing to optimize performance and facilitate analysis.

Cloud Environment Management:

• Work within cloud environments - Azure to deploy, manage, and optimize data engineering

solutions.

• Collaborate with cloud architects and administrators to ensure security, compliance, and cost-

effectiveness of cloud infrastructure.

Power BI Visualization and Reporting:

• Develop and maintain Power BI dashboards and reports to visualize AMI data insights and

facilitate data-driven decision-making.

• Ensure the accuracy, reliability, and usability of visualizations to meet business requirements.

Data Cataloging and Documentation:

• Catalog and document AMI data sources, datasets, and metadata to facilitate data discovery,

lineage, and governance.

• Implement data cataloging best practices to ensure the availability and accessibility of AMI data assets.

Collaboration and Support:

• Collaborate with cross-functional teams to understand data requirements and support analytical initiatives.
• Provide technical support and troubleshooting for data-related issues, ensuring the reliability and availability of data infrastructure.