Data Engineer

• Build and maintain data engineering pipelines, with a focus on unstructured data.
• Conduct requirements gathering and scoping sessions with business users and stakeholders to define GenAI-related data needs.
• Design, build, and optimize data architecture and ETL pipelines for accessibility by Data Scientists and GenAI products.
• Manage the full data lifecycle: ingestion, transformation, and consumption.
• Ensure high standards of data reliability, integrity, and governance.
• Work with APIs to enable seamless data integration and usability.
• Create technical design documentation for data pipelines and projects.
• Debug technical issues and manage code versioning using Git.
• Demonstrate experience with big data infrastructure such as MapReduce, Hive, HDFS, YARN, HBase, MongoDB, DynamoDB, etc.

Secondary Responsibilities

• Apply machine learning and predictive analytics techniques where relevant.
• Leverage domain knowledge in banking or financial services to enhance data solutions.
• Present data insights using effective storytelling and visualization techniques.

What We Are Looking For

Education

• Bachelor’s or master’s degree in computer science or Data Engineering or Information Systems or a related field.

Experience

• 2+ years of Proven experience in building and managing data pipelines and architectures.
• Hands-on experience with big data technologies and cloud platforms (AWS, GCP, or Azure).
• Exposure to GenAI applications and working with unstructured data formats.

Skills and Attributes

• Strong programming and debugging skills.
• Proficiency in data architecture, ETL design, and pipeline optimization.
• Familiarity with API integration and cloud services.
• Ability to work collaboratively with cross-functional teams.
• Excellent documentation and communication skills.
• Strong problem-solving mindset and attention to detail.
• Ability to deliver high-quality outputs under tight timelines.