We are seeking an experienced Data Engineer with expertise in AWS to design, build, and maintain robust data pipelines and workflows. This role requires close collaboration with data scientists, analysts, and product teams to deliver scalable data models and reliable datasets that advance experimentation efforts.
Responsibilities
- Design, develop, and maintain efficient data pipelines and workflows within AWS environments.
- Transform, model, and standardise raw data into reliable, analysis-ready datasets following best practices in data modelling and governance.
- Collaborate with data science, analytics, and product teams to translate business requirements into scalable data models and dependable datasets.
- Manage and optimise SQL-based ETL processes to ensure efficiency, reliability, and consistency across experimentation projects.
- Implement and uphold data quality standards through rigorous testing, validation, and monitoring frameworks.
- Document data flows, schemas, and pipeline logic to ensure transparency and maintainability.
- Partner with Analytics and Experimentation teams to design experiment tracking data structures and pipelines that capture key performance indicators.
- Contribute to the ongoing improvement of data standards, naming conventions, and reusable frameworks within the product data domain.
Qualifications
- Minimum of three years’ experience in a Data or Analytics Engineering role, preferably supporting digital product experimentation or data science workflows.
- Advanced proficiency in SQL for creating and optimising complex queries, data transformations, and data models.
- Strong understanding of data modelling techniques, including STAR schemas, third normal form (3NF), entity-relationship modelling, and Medallion architecture.
- Demonstrated experience designing and maintaining data pipelines within AWS services such as Redshift, S3, Glue, Lambda, and Step Functions.
- Familiarity with modern data orchestration and transformation tools, including DBT, Airflow, or equivalent technologies.
- Ability to analyse ambiguous business problems and develop clear, actionable data solutions.
- Excellent analytical and problem-solving capabilities, with a focus on building scalable, efficient, and transparent data solutions.
- Effective communication skills to engage with both technical and non-technical stakeholders.
- Knowledge of experimentation frameworks, including A/B testing pipelines and event tracking, is highly advantageous.
Benefits
- Opportunity to work with cutting-edge AWS technologies and data engineering tools.
- Collaboration with interdisciplinary teams including data scientists, analysts, and product managers.
- Engagement in impactful projects that drive data-driven experimentation and innovation.
- Supportive environment for professional growth and continuous learning.
- A distinctive TEAL organizational culture characterized by respect, relationship-building, and a non-corporate atmosphere.
- Agile work methodology with minimal bureaucracy.
- Health and well-being initiatives, including Luxmed Gold Extended medical care and Multisport Plus benefits.