Job Purpose
Responsible for designing, building, and optimizing scalable data infrastructure and pipelines that support the organization's analytics, data science, and AI capabilities. Serves as a senior technical contributor, driving engineering excellence, ensuring reliable and high-performing data solutions, and providing guidance to junior engineers.
Key Result Responsibilities
- Design and deliver complex, scalable data pipelines and infrastructure components end-to-end, from requirements through to production
- Own the technical design and implementation of data models across relational, NoSQL, and lakehouse architectures to support analytics and ML workloads
- Architect and optimize data flows across cloud platforms (Azure, AWS, or GCP), ensuring performance, cost-efficiency, and reliability
- Build and maintain robust orchestration workflows using tools such as Apache Airflow, dbt, or Azure Data Factory
- Define and implement data quality frameworks — validation rules, automated testing, monitoring, and alerting — across assigned pipeline domains
- Collaborate closely with Data Scientists and Analytics Engineers to ensure data infrastructure meets downstream consumption requirements
Key Result Responsibilities-Continued
- Contribute to architectural reviews and platform decisions, providing well-reasoned technical input and trade-off analysis
- Lead code reviews and enforce engineering best practices, documentation standards, and security and governance requirements
- Mentor Associate and mid-level Data Engineers, providing technical guidance and supporting their professional development
- Proactively identify platform improvements, technical debt, and opportunities to modernize the data stack
Qualifications (Academic, Training, Languages)
- Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Technology, or a related field.
- Proven experience working with large-scale cloud data platforms in a professional setting
- ITIL Certification is an advantage but not mandatory
- Fluent in English language.
- Expert-level SQL and strong Python skills; working knowledge of Spark, Scala, or Java is a plus
- Deep hands-on experience with cloud data services on Azure, AWS, or GCP (e.g., Azure Data Factory, Synapse, S3, Redshift, BigQuery)
- Advanced knowledge of data orchestration tools (e.g., Apache Airflow, dbt, Prefect) and CI/CD practices for data pipelines
- Solid understanding of dimensional and vault data modelling techniques and lakehouse architecture patterns
- Experience with streaming and batch processing frameworks (e.g., Apache Kafka, Spark Streaming, Azure Event Hubs)
- Strong grasp of data governance, data quality frameworks, and metadata management practices
- Ability to communicate complex technical designs clearly and mentor less experienced engineers effectively
- Strong experience with data warehousing platforms such as Snowflake, Redshift, or Azure Synapse, including performance tuning and access control
Work Experience
- With 6-8 years of progressive hands-on experience in data engineering, with a demonstrable track record of delivering production-grade data infrastructure
