About this Senior Engineer - Backend data role at Target
About Target
Target is an iconic brand, a Fortune 50 company, and one of America’s leading retailers.
Target as a tech company? Absolutely. We are the behind-the-scenes powerhouse that fuels Target’s passion and commitment to cutting-edge innovation. Our teams build and operate the technology that powers every part of Target’s 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 You’ll 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 Target's technology ecosystem.
- Bachelor's 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.
- 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.
