About The Position

We are sharing a full-time opportunity for an experienced Data Engineer with strong expertise in Python, SQL, ETL pipelines, data modelling, exploratory data analysis, and scalable data-processing workflows to support research, analytics, and AI/ML initiatives. The role focuses on transforming structured and unstructured information into reliable, production-quality datasets. The successful candidate will build scalable data pipelines, improve data quality and validation, optimise SQL and processing workflows, and collaborate with researchers, data scientists, and engineers.

Requirements

  • Strong proficiency in Python and SQL
  • Hands-on experience designing and maintaining ETL pipelines
  • Experience with exploratory data analysis
  • Proficiency with Pandas and NumPy
  • Experience with PostgreSQL, MySQL, or comparable relational databases
  • Strong understanding of data modelling and database schemas
  • Experience with structured and unstructured datasets
  • Strong focus on data quality, integrity, and reliability
  • Strong analytical and problem-solving skills
  • Experience collaborating with researchers, data scientists, or engineering teams
  • Familiarity with Jupyter Notebook, VS Code, PyCharm, or similar tools

Nice To Haves

  • Exposure to AI/ML workflows is advantageous
  • Familiarity with scikit-learn, Hugging Face Transformers, or AI APIs is beneficial

Responsibilities

  • Design, build, and maintain scalable ETL pipelines
  • Collect, clean, normalise, and transform structured and unstructured data
  • Automate recurring processing workflows
  • Monitor pipeline performance and troubleshoot failures
  • Improve efficiency, scalability, and maintainability
  • Write and optimise SQL queries for extraction and transformation
  • Work with relational databases such as PostgreSQL and MySQL
  • Design and maintain schemas and data models
  • Improve query performance and data-access patterns
  • Support scalable and reliable data-storage architectures
  • Conduct exploratory data analysis to identify patterns, anomalies, and quality issues
  • Build automated validation and quality-control workflows
  • Monitor accuracy, completeness, integrity, and consistency
  • Investigate data issues and implement corrective actions
  • Produce clear analytical summaries for technical stakeholders
  • Build processing workflows using Python, Pandas, and NumPy
  • Develop reusable components for transformation and analysis
  • Prepare datasets for AI and machine-learning initiatives
  • Collaborate with researchers and data scientists on data requirements
  • Support reliable training, evaluation, and experimentation workflows

Benefits

  • Full-time engagement
  • Fully remote
  • Compensation: $140,000–$180,000/year
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