Data Engineer ( Early career

Experience Level: 1–2 Years

We are seeking a highly motivated, early-career Data Engineer to support a Life Sciences client in building and maintaining data pipelines, transforming real-world data into analytical-ready datasets, and supporting downstream analytics and reporting. This role is ideal for someone who enjoys hands-on coding, problem-solving, and learning in a fast-paced, data-driven environment.

The ideal candidate brings strong foundational technical skills, a logical and analytical mindset, and a genuine interest in growing long-term within data engineering and analytics. The client is willing to invest in training for candidates who demonstrate the right skills, curiosity, work ethic, and attitude.

• Bachelor’s and Master’s degree in Computer Science, Engineering, Data Science, Analytics, Economics, Statistics, Biostatistics, or a related quantitative field
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• 1–2 years of hands-on experience in data engineering, analytics, software development, or related roles (internships and academic projects may be considered).
• Strong working knowledge of SQL (required).
• Experience coding in Python and/or R.
• Experience working with large datasets and data transformation workflows.
• Strong logical thinking and problem-solving skills.
• Ability to understand and implement business rules and data logic accurately.
• Self-driven, highly motivated, and eager to learn in a long-term role.
• Fast learner with strong attention to detail and quality mindset.
• Strong communication and collaboration skills.

• Design, build, and maintain data pipelines to transform raw data into clean, structured analytical datasets.
• Work with large datasets and apply data algorithms to support analytical workflows.
• Develop scripts and workflows using Python or R for data processing and automation.
• Write and optimize complex SQL queries for data extraction, transformation, and validation.
• Ensure data accuracy, consistency, and performance across pipelines.

• Support the development of dashboards and reports for internal and client stakeholders.
• Assist in translating business logic and rules into technical implementations.
• Perform quality control checks and data validation to ensure reliable outputs.
• Participate in exploratory analysis and research activities as needed.

• Quickly ramps up on data pipelines, tools, and business logic.
• Produces clean, accurate, and reliable analytical datasets.
• Writes efficient, maintainable code and SQL queries.
• Demonstrates ownership, curiosity, and continuous improvement.
• Collaborates effectively with team members and contributes ideas.
• Grows into increased technical responsibility over time.