Lead Engineer, EDT Data & Analytics - Data Engineering

Overview

Lead the design, development and optimization of complex, high-scale data pipelines and platform solutions across the Data and Analytics organization, providing technical leadership and mentoring junior engineers.

What you'll do

• Lead the architecture and optimization of scalable ETL/ELT pipelines, data models, and cloud-based data platforms to power enterprise analytics.
• Drive engineering excellence through automation, orchestration, CI/CD, performance tuning, and adoption of best practices across the data landscape.
• Establish robust data quality, governance, security, and monitoring frameworks to ensure reliable and trusted data solutions.
• Mentor engineering teams and collaborate with business, product, and architecture stakeholders to deliver high-impact, scalable data products.
• Design scalable batch and streaming data architectures, delivering high-performance and cost-efficient data solutions.
• Partner with Data Architecture, Analytics, and Platform teams to define engineering standards and drive best practices.
• Manage multiple priorities, mitigate risks, reduce technical debt, and deliver strategic outcomes with minimal oversight.

What you'll need

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related discipline.
• 6-8 years of experience in data engineering, data platform development, or related technical roles.
• Advanced expertise in SQL, Python/Scala/Java, and cloud-based data engineering, with a proven track record of building scalable, production-grade data pipelines and optimizing modern data platforms.
• Deep experience with cloud data platforms such as Snowflake, Databricks, AWS Redshift, or Azure Synapse.
• Advanced experience with orchestration tools such as Apache Airflow, Azure Data Factory, or dbt at enterprise scale.
• Experience with streaming/event-driven architectures such as Kafka, Kinesis, or Event Hubs.
• Strong background in CI/CD, infrastructure-as-code, and containerization (Docker/Kubernetes) for data platforms.
• Advanced knowledge of data modeling techniques (dimensional modeling, star schema, data vault) and metadata/data governance practices.
• Knowledge of Data Lake, Lakehouse and Unity Catalogue concepts.
• Use of tools' AI features to bring efficiency in coding and bug fixing.
• Strong technical leader who mentors engineers, drives architectural decisions, and translates complex concepts for diverse stakeholders.

Nice to have

• Advanced cloud platform certification (AWS, Azure, or GCP Data Engineer/Architect track).
• Snowflake, Databricks, or equivalent advanced data platform certification.

Details

• Location: Gurugram, India.
• Work mode: Hybrid.
• Travel: None.