Data Engineer

Accenture Technology powers our clients to achieve high performance. We combine business and industry insights with innovative technology to drive growth for your business. We extend our technology and business capabilities through a powerful alliance ecosystem of market leaders and innovators to provide our clients the best specialized skills and tailored solutions.

Job Summary

Accenture is looking for Data Engineers to help us with design, develop, and maintain data solutions. Applicants must have experience in Python and SQL.

Responsibilities

• Design, build, and maintain data pipelines and workflows for structured and unstructured data
• Develop and optimize ETL/ELT processes using Python and SQL
• Contribute to the development of internal tools and applications (Python-based)
• Apply best practices for data governance, security, and data quality in day-to-day activities.
• Managing databases and ensuring data quality and reliability.
• Collaborate with cross-functional teams to understand data requirements and implement effective solutions.
• Stay up to date with industry trends, tools, and emerging technologies to continuously improve technical skills and data processes

Qualifications

• 3+ years of experience in Data Engineering.
• Strong proficiency in Python and SQL for data manipulation and transformation (required).
• Knowledge of Data Engineering concepts, including data ingestion, transformation, and pipeline monitoring.
• Understanding of data governance, security, and data quality principles.
• Ability to work collaboratively with team members and cross-functional teams.
• Fluency in English (spoken and written).

Nice to have (any of them )

• Experience with cloud platforms such as AWS, Azure, GCP, or Snowflake.
• Familiarity with big data tools.
• Hands-on experience with APIs (e.g., RESTful APIs, GraphQL) and system integration.
• Knowledge of microservices architecture and containerization tools like Docker or Kubernetes.
• Exposure to machine learning workflows, including model training, evaluation, and deployment.
• Understanding of data modeling, data lakes, and data warehouses.