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.
Skills Required
- Bachelor’s degree in Computer Science, Engineering, Information Technology, or equivalent practical experience
- 5+ years of experience in software or data engineering
- Significant experience building and supporting large-scale data platforms
- Hands-on experience with Apache Spark
- Hands-on experience with Scala
- Hands-on experience with Google BigQuery
- Experience with Big Data and distributed processing
- Experience with data warehousing and ETL/ELT
- Strong SQL skills and experience with large, complex datasets
- Experience designing and optimizing high-volume, high-performance data pipelines
- Understanding of distributed computing, partitioning, joins, aggregation, scalability, and performance optimization
- Experience with cloud-based data platforms, preferably Google Cloud Platform
- Understanding of data modeling, including fact/dimension models and analytical data structures
- Experience with data quality, monitoring, observability, and production support
- Strong problem-solving and complex technical troubleshooting skills
- Ability to participate in architecture discussions and translate business requirements into scalable technical solutions
- Strong communication and collaboration skills
- Experience with Java and Spring Boot
- Experience building REST APIs or microservices
- Experience with 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 or GitHub Actions
- Experience with Docker or other containerization technologies
- Experience with Monte Carlo or equivalent data quality platforms
- Exposure to Generative AI and AI-assisted development tools such as GitHub Copilot or Claude
- Experience developing or contributing to reusable data engineering frameworks
- Experience in Retail, AdTech, Media, Analytics, or other high-volume data domains
Target Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Target and has not been reviewed or approved by Target.
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Healthcare Strength — Health benefits are accessible to hourly team members at relatively low hour thresholds and include no‑cost, 24/7 virtual medical care and expanded mental‑health support. This breadth is positioned as a relative strength compared to typical retail offerings.
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Retirement Support — Retirement programs include a dollar‑for‑dollar 401(k) match with immediate vesting and options like Roth 401(k) and stock purchase. These features strengthen long‑term savings for a wide range of roles.
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Parental & Family Support — Family support includes paid family leave, backup care, and reimbursements for adoption and surrogacy. These resources complement paid time off and holidays for eligible team members.
Target Insights
What We Do
Target is an American retailing company providing access to a wide selection of products such as furniture, electronics, toys, and more. Target is one of the world’s most recognized brands and one of America’s leading retailers. We make Target our guests’ preferred shopping destination by offering outstanding value, inspiration, innovation and an exceptional guest experience that no other retailer can deliver. Target is committed to responsible corporate citizenship, ethical business practices, environmental stewardship and generous community support. Since 1946, we have given 5 percent of our profits back to our communities. Our goal is to work as one team to fulfill our unique brand promise to our guests, wherever and whenever they choose to shop.
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