Data Engineer

Data Engineer

Req number:

R4505

Employment type:

Full time

Worksite flexibility:

HybridWho we are

CAI is a global technology services firm with over 8,500 associates worldwide and a yearly revenue of $1 billion+. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients, colleagues, and communities. As a privately held company, we have the freedom and focus to do what is right—whatever it takes. Our tailor-made solutions create lasting results across the public and commercial sectors, and we are trailblazers in bringing neurodiversity to the enterprise.

Job Summary

We are looking for a motivated Data Engineer ready to take us to the next level! If you have of experience in Azure Data Factory (ADF), Databricks, Python, and SQL, along with solid knowledge of DevOps practices. and are looking for your next career move, apply now. 

Job Description

We are looking for a Data Engineer. This position will be full-time and Hybrid.

What You’ll Do

• Develop and Maintain Data Pipelines: Design, develop, and manage complex ETL/ELT processes using Azure Data Factory (ADF), ensuring the timely and accurate ingestion of data from multiple sources.
• Big Data Processing with Databricks: Utilize Databricks for large-scale data processing, data transformation, and advanced analytics, leveraging Spark for real-time data processing.
• Database Management: Write and optimize complex SQL queries for data extraction, transformation, and analysis across different databases (e.g., Azure SQL, Synapse, PostgreSQL).
• Data Modeling: Design and implement data models to support scalable data pipelines and analytical reporting needs.
• Python Scripting: Develop and maintain Python scripts for data processing, automation of workflows, and integration with APIs and other services.
• Implement DevOps Practices: Work with DevOps tools and practices (e.g., Azure DevOps, Git, CI/CD pipelines) to automate data workflows, ensure version control, and manage deployments.
• Data Quality & Governance: Ensure data accuracy and consistency through data validation, quality checks, and adherence to data governance policies.
• Collaboration: Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements and provide solutions that align with business objectives.

What You'll Need

• 5+ years of experience as a Data Engineer or in a similar role.
•  Strong hands-on experience with Azure Data Factory (ADF) for building and managing data pipelines. Expertise in Databricks for data processing and transformation, with a deep understanding of Apache Spark.
•  Proficiency in Python for scripting, data processing, and building custom ETL/ELT logic.
• Advanced knowledge of SQL for querying, data manipulation, and performance tuning.
•  Experience with DevOps practices, including CI/CD pipelines, version control (Git), and automated deployments.
• Understanding of data architecture, data warehousing, and data modeling concepts.
• Excellent problem-solving skills, with the ability to work independently as well as part of a team.

Preferred Skills

•  Experience with APIs and RESTful services for data integration.
• Knowledge of big data tools and frameworks beyond Databricks, such as Hadoop, Kafka, or Airflow.
• Experience with containerization technologies like Docker and Kubernetes.
• Certifications in Microsoft Azure (e.g., Azure Data Engineer Associate, Azure Solutions Architect) are a plus

Physical Demands

• Sedentary work that involves sitting or remaining stationary most of the time with occasional need to move around the office to attend meetings, etc.
• Ability to conduct repetitive tasks on a computer, utilizing a mouse, keyboard, and monitor.

Reasonable accommodation statement

If you require a reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employment selection process, please direct your inquiries to application.accommodations@cai.io or (888) 824 – 8111.