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Job description
About the role
We are seeking a Data Engineer to design, build, and optimize modern data platforms that enable analytics, reporting, operational intelligence, and AI-driven solutions.
In this role, you will work across the full data lifecycle, developing scalable data pipelines, integrating complex data sources, and delivering high-quality data solutions that support business and customer objectives. You will collaborate with architects, engineers, analysts, and stakeholders to modernize data environments and implement cloud-based data platforms.
The ideal candidate is both a strong technical practitioner and a problem solver who enjoys working with modern data technologies, navigating ambiguity, and delivering scalable solutions in dynamic environments.
What you'll do
Serve as the technical leader for our data engineering initiatives.
Design, develop, and maintain scalable data pipelines that ingest, transform, and deliver data from structured, semi-structured, and unstructured sources.
Build and support ETL/ELT solutions across cloud-based data platforms, data warehouses, lakehouses, and analytics environments.
Develop data integration frameworks that enable reliable, secure, and efficient movement of data across systems.
Design and implement dimensional models, transformation logic, and data structures that support analytics, reporting, and AI initiatives.
Work with modern cloud data technologies such as Microsoft Fabric, Databricks, Snowflake, and related platforms.
Collaborate with architects and stakeholders to translate business requirements into scalable technical solutions.
Apply data quality, testing, monitoring, lineage, and governance best practices throughout the development lifecycle.
Optimize data pipelines and data platforms for performance, scalability, reliability, and cost efficiency.
Support modernization initiatives involving legacy data platforms, data warehouses, and integration technologies.
Implement modern engineering practices including source control, automated testing, deployment automation, and CI/CD processes.
Troubleshoot and resolve complex technical issues across data platforms and integration environments.
Contribute to architecture reviews, solution design discussions, and continuous improvement initiatives.
Stay current with evolving technologies, industry trends, and emerging approaches within data engineering, analytics, and AI.
Qualifications
6+ years of experience in Data Engineering, Data Integration, Analytics Engineering, Software Engineering, or a related technical discipline.
Strong experience designing, building, and supporting production-grade ETL/ELT pipelines and data integration solutions.
Advanced SQL skills, including query optimization, performance tuning, and data modeling.
Exposure to cloud platforms such as AWS, Azure, or GCP
Solid experience developing data solutions using Python, Scala, Java, or similar programming languages.
Experience working with cloud-based data platforms, data warehouses, lakehouses, and modern analytics environments.
Experience with distributed data processing technologies such as Spark or equivalent frameworks.
Hands-on experience with modern data platforms such as Microsoft Fabric, Databricks, Snowflake, BigQuery, Redshift, or similar technologies.
Familiarity with Git-based development workflows and CI/CD practices.
Understanding of data quality, governance, lineage, security, and observability concepts.
Ability to communicate effectively with both technical and non-technical stakeholders.
Strong analytical, troubleshooting, and problem-solving skills.
Ability to adapt to changing priorities, learn new technologies quickly, and succeed in fast-paced environments.
Preferred Qualifications
Experience implementing solutions using Microsoft Fabric, Databricks and Snowflake in production environments.
Experience with Databricks and modern lakehouse architectures.
Experience with dbt or similar transformation frameworks.
Experience modernizing enterprise data warehouses and legacy integration environments.
Experience with Azure Data Services or equivalent cloud-native data technologies.
Experience supporting machine learning, artificial intelligence, or advanced analytics initiatives.
Familiarity with semantic modeling, analytics-ready data structures, and data product concepts.
Experience implementing data governance, metadata management, or data lineage solutions.
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?
