Description

Key Skills: AWS Cloud, Team Management, Databricks, Data modeling, Data Warehouse, ETL, Data Engineering, AWS S3

Roles and Responsibilities:

• Lead, coach, mentor and grow a high-performing team of data engineers through hiring, performance management and career development.
• Define and execute the technical roadmap for enterprise data platforms, aligning priorities with product strategy and business outcomes.
• Oversee the design and operation of scalable ETL/ELT pipelines supporting batch, streaming and near-real-time data processing.
• Architect and evolve data warehouse, lakehouse, ingestion, transformation and integration capabilities on AWS Cloud and Databricks.
• Drive data quality, governance, metadata, privacy, security, observability, automation and operational reliability across data platforms and products.
• Partner with product management, software engineering, analytics, data science, architecture, security and business stakeholders to deliver analytics-ready data solutions.
• Enable reusable data products and reliable data foundations for AI/ML, reporting and advanced analytics use cases.
• Own delivery planning, backlog prioritization, resource planning, stakeholder communication and operational stability for the data engineering team.

Skills Required:

• 10 - 14 years of experience in data engineering, including at least 3 years leading, hiring, coaching and developing technical teams.
• Strong experience leading data engineering teams and delivering production-grade enterprise data platforms.
• Deep expertise in ETL/ELT, data warehousing, data engineering architecture and data modeling.
• Practical experience designing scalable data solutions using AWS Cloud and Databricks.
• Ability to lead architecture decisions involving data ingestion, transformation, integration, governance and platform reliability.
• Experience delivering secure, fault-tolerant and scalable data pipelines for analytics, reporting and AI/ML workloads.

Good to Have:

• Hands-on experience with AWS S3 and cloud-based data lake architectures.

Education: Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Mathematics or a related technical field, or equivalent practical experience

Key Skills

Education

Any Graduate