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Metro location, broad skillset, and a popular data engineering title increase applicant competition.
Core data engineering skills are highly transferable across industries.
Extensive mandatory tech stack and domain-specific data engineering skills make shortlisting stringent.
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Develop and maintain Snowflake-based data products, ELT pipelines using dbt, and implement automated data quality and monitoring for reliable datasets.
Design scalable, maintainable, performant, and cost-optimized data solutions including real-time data integration with Kafka and event-driven architectures.
Support platform modernization, enforce CI/CD practices, collaborate with stakeholders to deliver fit-for-purpose data solutions, and mentor junior engineers.
Mandatory skills: Advanced SQL, Python, Snowflake, dbt, ELT/ETL design, data modelling (Dimensional, Kimball, Data Vault), data warehousing and lakehouse concepts, Kafka fundamentals, AWS, CI/CD pipelines, Terraform, Git.
Experience with data quality engineering, data observability, API integrations, containerization (Docker), event-driven architectures, CDC concepts, and test-driven engineering.
Work Experience Required: Not explicitly mentioned in the JD.
Must be able to work in agile delivery environments and comply with GDPR, security, and data management standards.
Experienced data engineer skilled in building and optimizing enterprise-grade data platforms with a strong focus on performance, cost, and scalability.
Comfortable with real-time data integration using Kafka and cloud-based infrastructure automation (AWS, Terraform).
Experienced in mentoring and collaboration across technical and business teams, adept at delivering well-governed, fit-for-purpose data assets for advanced analytics and AI initiatives.