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Job description
Work Flexibility: Hybrid
The Lead Data Engineer leads the design, development, and optimization of complex, high-scale data pipelines and platform solutions across the Data and Analytics organization. This role provides technical leadership, mentors junior engineers, and partners with Data Architecture, Analytics, and Platform teams to define engineering standards, drive best practices, and deliver reliable, well-governed data solutions that support enterprise analytics and reporting needs.
What you will 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.
Expert in designing scalable batch and streaming data architectures, delivering high-performance and cost-efficient data solutions.
Advanced proficiency in SQL, Python, Scala, and distributed processing frameworks with deep expertise in cloud data platforms.
Strong technical leader who mentors engineers, drives architectural decisions, and translates complex concepts for diverse stakeholders.
Proven ability to manage multiple priorities, mitigate risks, reduce technical debt, and deliver strategic outcomes with minimal oversight.
What you will need :
Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related discipline required with 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.
Strong technical leader with deep knowledge of data architecture and modeling, recognized for mentoring teams, driving design excellence and managing stakeholders to deliver high-impact business outcomes.
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, and 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 Tool’s AI features to bring efficiency in Coding and bug fixing
Advanced cloud platform certification (AWS, Azure, or GCP Data Engineer/Architect track) preferred./ Snowflake, Databricks, or equivalent advanced data platform certification preferred.
This page is for Indian job seekers preparing for cloud engineer, DevOps, backend and solutions architect roles that list AWS as a skill, whether you are a fresher with a first certification or an experienced engineer moving into a senior role. It covers 60 questions in three levels: core services and concepts, then design and configuration choices for one to three years of experience, and finally scenario, security, cost and troubleshooting questions asked in senior rounds. Read each answer, explain it aloud in your own words, and try the CLI commands in a practice account with a billing alert set.
Questions you may be asked
What is the difference between an AWS Region and an Availability Zone?
Explain the AWS shared responsibility model.
What is Amazon EC2, and how do you read an instance type name like m7g.large?
What is Amazon S3, and what are its main storage classes?
