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Job description
Work Flexibility: Hybrid
The Data Engineer designs, builds, and maintains scalable data pipelines and integration solutions that power analytics, reporting, and business decision-making. This role works closely with Data Architects, Analysts, and Platform teams to ingest, transform, and deliver reliable, high-quality data across the Data and Analytics ecosystem, while following established engineering standards and best practices.
What you will do:
Design and optimize scalable ETL/ELT pipelines, data models, and data marts to enable reliable analytics and reporting.
Develop automated data workflows using orchestration tools while building efficient SQL and Python/Scala-based transformation processes.
Ensure data quality, governance, security, and compliance through robust validation, monitoring, and error-handling frameworks.
Support cloud data platforms and collaborate with stakeholders to deliver high-performance, business-driven data solutions.
Strong expertise in data engineering, including batch/streaming processing, SQL, and programming with Python, Scala, or Java.
Hands-on experience with distributed data frameworks, cloud data platforms, and modern data warehouse/lakehouse technologies.
Proven ability to troubleshoot complex technical issues, ensure data quality, and deliver reliable, scalable data solutions.
Collaborative and adaptable professional who works effectively across teams, embraces new technologies, and independently drives results.
What you will need:
Required Skills:
Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related discipline required with 3-5 years of experience in data engineering, ETL development, or database development.
Strong proficiency in SQL and hands-on experience with at least one programming language (Python, Scala, or Java).
Working knowledge of relational databases and data warehousing/lakehouse concepts.
Ability to analyze complex data requirements and translate them into scalable technical solutions.
Experience with cloud data platforms such as Snowflake, Databricks, AWS Redshift, or Azure Synapse along with hands on experience in building and maintaining data pipelines on-premises or in a cloud environment.
Experience with workflow orchestration tools such as Apache Airflow, Azure Data Factory, or dbt. And streaming technologies such as Kafka, Kinesis, or Event Hubs.
Familiarity with CI/CD practices and version control (Git) for data engineering workflows and understanding of data modeling techniques (dimensional modeling, star schema, data vault)., knowledge of Data Lake, Lakehouse and Unity Catalogue Concepts, use of Tool’s AI features to bring efficiency in Coding and bug fixing
Cloud platform certification (AWS, Azure, or GCP Data Engineer track) preferred./ Snowflake, Databricks, or equivalent 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?
