Data Engineer

Role Overview:

The Data Engineer will design, build, and maintain the data infrastructure that powers analytics and AI-driven features across the product. The role will combine strong engineering capabilities with hands-on ownership of data pipelines, warehouses, and integrations, working closely with engineering and business teams to build reliable, scalable data pipelines and ensure high-quality data is available for downstream systems.

Role & responsibilities:

1. Data Pipeline Engineering

• Design, build, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
• Develop and maintain infrastructure for real-time and batch data processing.
• Build and optimise data models and schemas to support reporting and experimentation.

2. Data Warehousing & Infrastructure

• Architect and manage data warehouses, data lakes, and database systems for analytics and ML workloads.
• Optimise query performance and manage cost/efficiency of data storage and compute.
• Work with cloud platforms (AWS, GCP, or Azure) to build and maintain data infrastructure.

3. Data Quality & Governance

• Implement data quality checks, monitoring, and alerting to ensure pipeline reliability and data integrity.
• Apply data governance, security, and privacy best practices across systems.

4. Integration & Automation

• Build and orchestrate ETL/ELT pipelines using tools such as Airflow, dbt, or equivalent.
• Develop web and app data scraping/extraction solutions (BeautifulSoup, Scrapy, Selenium, or similar).
• Integrate APIs and third-party data sources into the data ecosystem.
• Support LLM/AI pipelines with data feeds or vector stores.

5. Cross-Functional Collaboration

• Partner with engineering and business teams on real-world use cases such as event tracking, customer 360 pipelines, personalisation data feeds, and automation.
• Translate business and product requirements into robust, scalable data infrastructure.

6. Continuous Improvement

• Stay current with advancements in data engineering, cloud data platforms, and AI infrastructure.
• Identify opportunities to adopt streaming platforms, big data tools, and containerisation where relevant.

Preferred candidate profile

1. Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field.

2. Hands-on experience building and maintaining production-grade ETL/ELT pipelines and data warehouses.

3. Experience with cloud data platforms (AWS, GCP, or Azure).

Exposure to supporting LLM/AI pipelines or automation use cases will be an added advantage.