Lead Data Engineer

• Define and own the data engineering roadmap, aligning pipeline, platform, and infrastructure strategy with organizational data and AI goals
• Architect and oversee the design of scalable, resilient data pipelines, data lakes, and warehouse solutions that meet performance, cost, and governance requirements
• Lead end-to-end delivery of complex data engineering initiatives, from requirements through to production
• Establish and enforce engineering standards, code quality practices, and data governance frameworks across the team
• Evaluate and introduce new tools, frameworks, and cloud-native technologies to continuously improve the data platform
• Partner with Data Science and AI teams to ensure data infrastructure is fit for advanced analytics and machine learning workloads
• Collaborate with business stakeholders to translate data requirements into robust, reusable engineering solutions
• Lead, mentor, and develop a team of data engineers, supporting career growth and building a high-performance engineering culture

Key Result Responsibilities-Continued

• Own data platform reliability, monitoring, and incident response; drive root-cause resolution and preventive improvements
• Provide direction on data security, access control, and compliance in coordination with relevant governance functions

People Management: 

• Provides direction, coaching and guidance to team members for their career and professional development.  
• Creates a conducive working environment to build and sustain a performance driven, engaged, and committed team.   
• Ensures people management responsibilities are handled effectively in line with company procedures.

Qualifications (Academic, training, languages)

• Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Technology, or a related field. 
• Expert-level proficiency in SQL and Python; strong knowledge of Spark, Scala, or Java
• Deep experience with cloud data platforms (AWS, GCP, or Azure) and associated managed services
• Proven ability to design and implement complex data models across relational, NoSQL, and lakehouse architectures
• Advanced knowledge of data orchestration tools (e.g., Apache Airflow, Prefect, dbt) and CI/CD for data pipelines
• Strong understanding of data governance, data quality frameworks, and metadata management
• Familiarity with MLOps patterns and infrastructure requirements for machine learning workloads
• Ability to communicate complex technical concepts clearly to non-technical stakeholders and senior leadership
• Demonstrated leadership in building and mentoring engineering teams
• Strong commercial awareness and ability to make pragmatic engineering trade-offs aligned with business value
• Fluent in English; proficiency in Hindi is an advantage

Work Experience

• With 8-10 years of progressive experience in data engineering, with at least 2 years in a technical lead or senior engineering role.
• Demonstrated track record of delivering large-scale data infrastructure in a cloud environment. ITIL Certification is an advantage but not mandatory
• Experience with real-time and streaming data platforms (e.g., Apache Kafka, Flink, Kinesis)