Your core responsibilities will include:
- Developing robust, scalable data pipelines for ingesting, transforming, and storing data from multiple structured and unstructured sources using Python/SQL
- Creating and optimizing data models and data warehouses to support reporting, analytics, and application integration
- Working with cloud-based data platforms (AWS, Azure, or GCP) to build modern, efficient, and secure data solutions
- Contributing to R&D projects and internal asset development
- Contributing to infrastructure automation and deployment pipelines using containerization and CI/CD tools
- Collaborating across disciplines to integrate data engineering best practices into broader analytical and generative AI (gen AI) workflows
- Supporting and maintaining data assets deployed in client environments with a focus on reliability, scalability, and performance
- Furthermore, you will have the opportunity to explore and contribute to solutions involving generative AI, such as vector embeddings, retrieval-augmented generation (RAG), semantic search, and LLM-based prompting, especially as we integrate gen AI capabilities into our broader data ecosystem
Your qualifications and skills
- Bachelor’s degree in computer science, engineering, mathematics, or a related technical field (or equivalent practical experience).
- 3+ years of experience in data engineering, analytics engineering, or a related technical role.
- Strong Python programming skills with demonstrated experience building scalable data workflows and ETL/ELT pipelines.
- Proficient in SQL with experience designing normalized and denormalized data models.
- Hands-on experience with orchestration tools such as Airflow, Kedro, or Azure Data Factory (ADF).
- Familiarity with cloud platforms (AWS, Azure, or GCP) for building and managing data infrastructure.
- Discernable communication skills, especially around breaking down complex structures into digestible and relevant points for a diverse set of clients and colleagues, at all levels.
- High-value personal qualities including critical thinking and creative problem-solving skills; an ability to influence and work in teams.
- Entrepreneurial mindset and ownership mentality are must; desire to learn and develop, within a dynamic, self-led organization.
- Hands-on experience with containerization technologies (Docker, Docker-compose).
- Hands on experience with automation frameworks (Github Actions, CircleCI, Jenkins, etc.).
- Exposure to generative AI tools or concepts (e.g., OpenAI, Cohere, embeddings, vector databases).