Data Engineer

Role Synopsis:

The Data Engineer will be responsible for building the data foundation required to support AI-driven solutions across the organization. This role focuses on collecting, structuring, and transforming fragmented and unstructured data into reliable, accessible, and usable formats.

Working closely with AI engineers and business teams, the Data Engineer will design and maintain data pipelines, standardize datasets, and enable efficient data access for analytics and AI applications. The role requires a practical, hands-on approach to handling real-world data challenges and ensuring data quality in a complex environment.

Key Accountabilities:

• Design, build, and maintain data pipelines to ingest and process data from multiple sources (Excel, PDFs, emails, internal systems)
• Clean, transform, and standardize structured and unstructured datasets
• Develop and maintain a centralized data layer (data warehouse or data lake)
• Structure and organize business-critical data such as contracts, procurement data, and project information
• Collaborate with AI engineers to enable data availability for AI use cases, including document indexing and retrieval systems
• Implement data organization standards, naming conventions, and basic governance practices
• Ensure data quality, consistency, and reliability across all datasets
• Monitor and optimize data pipeline performance and scalability
• Integrate data from various sources using APIs and automated workflows
• Document data models, pipelines, and processes for maintainability
• Identify and resolve data-related issues that impact business operations and AI solutions
• Support continuous improvement of data architecture and infrastructure

Desired background:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field
• 3–6 years of experience in data engineering, backend engineering, or related roles
• Proven experience building and maintaining data pipelines in real-world environments
• Experience working with structured, semi-structured, and unstructured data sources
• Strong experience in integrating data from multiple systems (files, APIs, databases)
• Familiarity with cloud platforms (AWS, Azure, or GCP)
• Experience working in environments with fragmented or inconsistent data
• Exposure to supporting analytics, reporting, or AI/ML initiatives
• Experience working in fast-paced, delivery-oriented environments
• Demonstrated ability to solve complex data challenges and improve data reliability
• Portfolio or examples of data pipeline projects (preferred)

Required skills:

• Strong programming skills in Python and SQL
• Experience designing and implementing ETL/ELT data pipelines
• Solid understanding of data modeling and schema design
• Experience working with relational databases (e.g., PostgreSQL, MySQL)
• Ability to clean, transform, and standardize messy datasets
• Experience integrating data from multiple sources (files, APIs, systems)
• Familiarity with cloud platforms (AWS, Azure, or GCP)
• Understanding of data storage solutions (data warehouses, data lakes)
• Experience handling unstructured and semi-structured data (documents, PDFs, Excel files)
• Knowledge of data quality management and validation techniques
• Familiarity with workflow orchestration tools (e.g., Airflow, Prefect) is a plus
• Understanding of basic data governance and access control practices
• Strong problem-solving skills and attention to detail
• Ability to collaborate effectively with AI engineers and business stakeholders