Lead the architecture and engineering of modern, scalable cloud-native data platform solutions leveraging modern frameworks and technologies
Design and implement robust data pipelines (batch and streaming) that support engineering metrics, software telemetry, and operational insights
Drive the modernization of legacy data infrastructure, supporting both batch and real-time data processing
Drive adoption of best practices in data engineering, including data modeling, data quality, data governance, and DevOps for data
Collaborate with Product Owners, Users, data analysts and Software engineers to understand data needs and deliver robust data pipelines
Evaluate ad introduce emerging technologies to improve the data platform’s agility, scalability, and performance
Establish and enforce architectural guidelines, design patterns, and standards across data engineering initiatives
Apply secure-by-design principles across data pipelines and storage, incorporating threat modeling and privacy-aware data handling. Ensure robust security, privacy, and compliance standards are embedded in data solutions
Champion the responsible adoption of Generative AI tools for code generation, pipeline optimization, metadata management, and data quality enhancement
Mentor and guide other engineers, play a key role in technical leadership and team development
Partner with product and engineering leaders to align data initiatives with business outcomes
Education*
NA
Experience Range*
15 - 20 Years
Foundational Skills*
Deep understanding of data modeling, ETL/ELT, data pipelines and data warehousing concepts
Proven expertise in building ELT/ETL pipelines using tools like Apache Spark, Airflow, Kafka, and cloud-native orchestration frameworks
Strong proficiency in SQL and programming languages such as Python or Java
Strong knowledge of threat modeling and data security practices to proactively identify and mitigate risks in data pipelines and storage systems
Practical use of Generative AI tools (e.g.: GitHub CoPilot) for accelerating development, testing, and pipeline refactoring
Proven leadership, collaboration, and communication skills to drive cross-functional initiatives
Ability to influence data strategy and architectural decisions while mentoring engineers, and foster a data-driven engineering culture
Desired Skills*
Experience modernizing data platforms using Lakehouse, data mesh, or event-driven architectural patterns
Working knowledge of data governance, privacy frameworks, and secure cloud deployments
Exposure to Kubernetes, Docker, and Observability practices in data engineering context
Nice to have experience with cloud-native data platforms (e.g.: Databricks, Azure Data services)
Exposure to AI/ML workflows, including integration with ML APIs, and orchestration of AI-powered features
Ability to evaluate and adopt Generative AI capabilities (e.g.: LLM Integration, AI-assisted coding, architecture suggestion engines) to enhance developer and user experiences