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Senior Engineer Backend data

Target

Job Location:

Bengaluru - India

Monthly Salary: Not provided by the employer

Posted: 7 September 2026 (Yesterday)

Application Deadline: 5 December 2026

Vacancies: 1 Vacancy

Job Summary

About Target
Target is an iconic brand a Fortune 50 company and one of Americas leading retailers.
Target as a tech company Absolutely. We are the behind-the-scenes powerhouse that fuels Targets passion and commitment to cutting-edge innovation. Our teams build and operate the technology that powers every part of Targets digital and store experience. We combine modern engineering practices cloud technologies data and innovative thinking to deliver reliable scalable solutions that create meaningful value for our guests and team members.
Our high-performing teams balance independence with collaboration and we pride ourselves on being versatile agile and creative. We are committed to building technology that operates smoothly securely and reliably while continuously exploring new ways to solve complex business problems.

About the Role
As a Senior Data Engineer you will play a key role in designing developing and optimizing large-scale data platforms and pipelines that support critical business and analytical use cases. You will work across the full data lifecycle from data ingestion and transformation to storage processing quality and consumption.
You will apply strong expertise in Big Data Apache Spark Scala and Google BigQuery to build high-performance scalable and reliable data solutions. You will work closely with engineers architects product teams data scientists and business partners to translate complex requirements into robust technical solutions.
The ideal candidate combines strong data engineering fundamentals with a passion for solving complex problems optimizing large-scale workloads and continuously learning emerging technologies. Experience with Java/Spring Boot and exposure to AI-assisted engineering tools such as GitHub Copilot and Claude will be an added advantage.
What Youll Do

Design develop and maintain scalable data pipelines and data processing frameworks using Spark Scala BigQuery and other Big Data technologies.
Build robust ETL/ELT pipelines for high-volume and complex datasets.
Develop efficient data models and optimize BigQuery tables using appropriate partitioning clustering query design and storage strategies.
Design and implement scalable batch and distributed data processing solutions using Apache Spark.
Develop reusable frameworks and components to improve engineering productivity data processing efficiency and operational reliability.
Analyze and optimize large-scale data workloads for performance scalability reliability and cloud cost efficiency.
Implement data quality validation monitoring and observability across data pipelines and datasets.
Troubleshoot complex data pipeline infrastructure and production issues and drive root-cause resolution.
Participate in architecture and design discussions and contribute to technical decisions involving data platforms and cloud technologies.
Collaborate with Product Analytics Data Science Architecture and other engineering teams to deliver high-quality data products.
Participate in code reviews design reviews testing debugging and production support activities.
Follow engineering best practices around CI/CD automation security reliability and operational excellence.
Evaluate and adopt emerging technologies that improve data engineering productivity and platform capabilities.
Contribute to technical documentation engineering standards and knowledge-sharing across the team.
Explore and adopt AI-assisted development tools such as GitHub Copilot Claude and similar solutions to improve developer productivity and engineering efficiency.
Identify practical opportunities to leverage Generative AI for code development code reviews debugging documentation data analysis and automation.
Stay current with emerging AI and data engineering technologies and evaluate their applicability to Targets technology ecosystem.

About You

Bachelors degree in Computer Science Engineering Information Technology or equivalent practical experience.
5 years of experience in software or data engineering with significant experience building and supporting large-scale data platforms.
Strong hands-on experience with:
- Apache Spark
- Scala
- Google BigQuery
- Big Data / Distributed Processing
- Data Warehousing
- ETL/ELT
Strong SQL skills and experience working with large and complex datasets.
Experience designing and optimizing data pipelines for high-volume high-performance processing.
Strong understanding of distributed computing concepts data partitioning joins aggregation scalability and performance optimization.
Experience with cloud-based data platforms preferably Google Cloud Platform (GCP).
Good understanding of data modeling including fact/dimension models and analytical data structures.
Experience with data quality monitoring observability and production support.
Strong problem-solving skills with the ability to independently troubleshoot complex technical issues.
Ability to participate in architecture discussions and translate business requirements into scalable technical solutions.
Strong communication and collaboration skills.

Preferred / Good-to-Have Skills

Java and Spring Boot experience.
Experience building REST APIs or microservices.
Kafka or other event-streaming technologies.
Experience with cloud-native architectures and GCP services.
Experience with Python and Shell scripting.
Experience with CI/CD tools such as Jenkins GitHub Actions or similar platforms.
Experience with containerization technologies such as Docker.
Experience with data quality platforms such as Monte Carlo or equivalent.
Exposure to Generative AI and AI-assisted development tools including GitHub Copilot Claude or similar tools.
Experience developing or contributing to reusable data engineering frameworks.
Experience in Retail AdTech Media Analytics or other high-volume data domains is a plus.