Engineer - Hardware Observability
Target is hiring an Engineer - Hardware Observability. It pays $75k-$136k a year and Level rates it ; you can apply on Target.
Level's read on this job
- Little AI ●○○○ is how Level rates this role: aI is not part of the work.
- Level classifies it as a mid-level role.
- Level lists 10,000+ other open roles at Target.
AI in this role
Pay is based on several factors which vary based on position. These include labor markets and in some instances may include education, work experience and certifications. In addition to your pay, Target cares about and invests in you as a team member, so that you can take care of yourself and your family. Target offers eligible team members and their dependents comprehensive health benefits and programs, which may include medical, vision, dental, life insurance and more, to help you and your family take care of your whole selves. Other benefits for eligible team members include 401(k), employee discount, short term disability, long term disability, paid sick leave, paid national holidays, and paid vacation. Find competitive benefits from financial and education to well-being and beyond at https://corporate.target.com/careers/benefits.
About us:
Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. Learn more about Target here.
As an Engineer, you serve as a technical specialist delivering the engineering that powers the product. You develop keen insight into the technical architecture and design to deliver robust and scalable software components. You constantly demonstrate the depth of your expertise by solving engineering problems. You are passionate about the quality of software and balance between speed of delivering new features and robustness of the software components you implement. You can handle operational issues with little or no oversight. You actively review code to ensure the software quality and functional accuracy is maintained across the team. You are keen to learn the design and architecture of the product and participate in ceremonies that can influence both.
Use your skills, experience, and talents to be a part of groundbreaking thinking and visionary goals. As an engineer, you’ll take the lead as you…
Use your data engineering skills to support Target’s Infrastructure organization by helping build, maintain, and improve reliable data pipelines for hardware observability data. Partner with engineering consumers across SecOps, DevOps, and Observability use cases to ensure data is available, trusted, and easy to use. With guidance, develop and maintain data schemas, ETL processes, validation checks, and pipeline tooling that collect, transform, and deliver infrastructure telemetry and related metadata. Monitor data quality, investigate common data issues, and assist with root cause analysis when pipelines, data feeds, or quality checks fail. Contribute to documentation for data definitions, lineage, standards, and operational runbooks so teams can understand and use hardware observability data effectively. Participate in code reviews, testing, deployment, change management, and incident response activities while learning Target’s engineering practices, infrastructure platforms, and data governance expectations.
Core responsibilities are described within this job description. Job duties may change at any time due to business needs.
About you:
• 4 year degree or equivalent experience
• 1+ years of data engineering, observability engineering, infrastructure data, or related technical experience
• Understands foundational data engineering concepts, including data schemas, data modeling, ETL/ELT patterns, batch and streaming data processing, and pipeline reliability
• Demonstrates proficiency in at least one general programming language (i.e., Python, Java, etc) and at least one domain-specific language used for data pipeline development
• Understands data quality concepts and can implement validation checks, monitoring, reconciliation, and issue investigation for observability data pipelines
• Knowledge of common data formats, metadata, lineage, and schema evolution practices, with the ability to apply them to hardware observability and infrastructure telemetry data
• Demonstrates familiarity with automation tools, network technology fundamentals, and SecOps practices
• Demonstrates familiarity with current and emerging technologies in own scope of responsibility, and develops ability to apply these technologies
This position will operate as a Hybrid/Flex for Your Day work arrangement based on Target’s needs. A Hybrid/Flex for Your Day work arrangement means the team member’s core role will need to be performed both onsite at the Target HQ MN location the role is assigned to and virtually, depending upon what your role, team and tasks require for that day. Work duties cannot be performed outside of the country of the primary work location, unless otherwise prescribed by Target. Click here if you are curious to learn more about Minnesota.
Benefits Eligibility
Please paste this url into your preferred browser to learn about benefits eligibility for this role: https://tgt.biz/BenefitsForYou_DAmericans with Disabilities Act (ADA)
In compliance with state and federal laws, Target will make reasonable accommodations for applicants with disabilities. If a reasonable accommodation is needed to participate in the job application or interview process, please reach out to candidate.accommodations@HRHelp.Target.com. Non-accommodation-related requests, such as application follow-ups or technical issues, will not be addressed through this channel.
How we rate this
Engineer - Hardware Observability at Target rates 31 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
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