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Data Jobs in Indiana, PA (NOW HIRING)

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Data information

See Indiana, PA salary details

$42K

$150.8K

$222.5K

How much do data jobs pay per year?

As of Sep 3, 2026, the average yearly pay for data in Indiana, PA is $150,817.00, according to ZipRecruiter salary data. Most workers in this role earn between $122,000.00 and $155,400.00 per year, depending on experience, location, and employer.

What are different jobs that work with data?

Many different jobs require you to work with data. Occupational health and safety engineers, for instance, assess safety data collected by technicians and specialists and then design new processes to mitigate observed risks. Many careers in medical research, such as running clinical trials or developing new pharmaceuticals, require data collection and analysis. A large number of government labor and economic forecasting positions employ statisticians who analyze and model data based on surveys or raw information, such as the census or employment records.

What are the key skills and qualifications needed to thrive as a data analyst, and why are they important?

To thrive as a Data Analyst, you need strong analytical skills, proficiency in statistics, and a solid foundation in mathematics, typically supported by a degree in a quantitative field. Familiarity with data analysis tools like Excel, SQL, Python, and visualization platforms such as Tableau or Power BI is often required, and certifications in these tools can be advantageous. Attention to detail, critical thinking, and effective communication skills help analysts interpret data accurately and present actionable insights to stakeholders. These skills are crucial for transforming raw data into meaningful information that drives informed business decisions.

How does a data analyst typically collaborate with other departments within an organization?

Data Analysts frequently work cross-functionally, partnering with teams such as marketing, finance, operations, and product development. They gather requirements from stakeholders, interpret data to provide actionable insights, and often present findings in meetings or reports tailored to the audience's needs. Effective communication is key, as analysts must translate complex data into clear, impactful recommendations that guide business decisions. This collaborative environment fosters both learning and professional growth, as Data Analysts gain exposure to various business functions.

What is the difference between Data vs Data Analyst?

AspectDataData Analyst
Required CredentialsTypically a degree in computer science, information technology, or related fieldsSame as Data, often requiring a degree in statistics, data science, or related areas
Work EnvironmentData professionals work in IT, data engineering, or database management settingsData analysts work in business, finance, marketing, and similar industries analyzing data for insights
Employer & Industry UsageUsed across tech, finance, healthcare, and more for data management and infrastructureCommonly employed in business sectors to interpret data and support decision-making

Data professionals focus on managing, storing, and processing data, while Data Analysts interpret and analyze data to generate insights. Both roles require similar educational backgrounds but differ in their primary functions within organizations.

What are careers in data?

Careers in data include roles such as data analyst, data scientist, data engineer, and database administrator. These jobs involve collecting, analyzing, and managing data using tools like SQL, Python, and data visualization software, often requiring strong analytical skills and knowledge of data management principles.

What data jobs are there?

Data jobs include roles such as data analyst, data scientist, data engineer, and database administrator. These positions typically require skills in programming, statistics, and data management tools like SQL, Python, or R, and may involve working with large datasets, data visualization, and machine learning techniques.

What job categories do people searching Data jobs in Indiana, PA look for?

The top searched job categories for Data jobs in Indiana, PA are:

What cities near Indiana, PA are hiring for Data jobs?

Cities near Indiana, PA with the most Data job openings:

Infographic showing various Data job openings in Indiana, PA as of August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 9% Part Time, and 2% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $150,817 per year, or $72.5 per hour.

Staff Agentic AI / Data Engineer

Advanced Micro Devices

Indiana, PA • On-site

$150 - $190/hr

Other

This job post has expired today. Applications are no longer accepted.


Advanced Micro Devices rating

8.6

Company rating: 8.6 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

26th of 161 rated electronics manufacturers


Job description

ADVANCE YOUR CAREER. ADVANCE THE WORLD.

At AMD, we believetechnology has the power to solve the world's most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMDis shapingthefuture.

Whetheryou’redesigning next-gen processors, enabling AI breakthroughs, orbringing leading edge products to market, every role at AMD contributes to something bigger— technologythat moves the world forward.Join us and, together, we’ll advance your career.

THE TEAM:

AMD's Data Center GPU organization is transforming the industry with our AI based Graphic Processors. Our primary objective is to design exceptional products that drive the evolution of computing experiences, serving as the cornerstone for enterprise Data Centers, (AI) Artificial Intelligence, HPC and Embedded systems.If this resonates with you, come and joining our Data Center GPU organization where we are building amazing AI powered products with amazing people.

THE ROLE:

AMD's Applied AI team works with the world's most demanding AI operators - frontier labs, NeoCloud providers, and AI-native companies - to make AMD Instinct GPU infrastructure the easiest place to build and run AI. As an Agentic Data Engineer, you will build the data and agent systems that sit at the heart of this mission: production agentic AI applications running on AMD clusters, the data pipelines and memory/context databases that give those agents durable knowledge, and the skills frameworks and evaluation infrastructure that make agent behavior reliable, measurable, and safe.

Your work spans two surfaces. Externally, you build agentic systems and their data foundations on customer AMD deployments - the reference implementations customers adopt when they move from inference to agents. Internally, you build the Applied AI team's own intelligence layer: engagement memory databases, fleet and telemetry data pipelines, and agent-executable skills libraries that encode deployment knowledge so every customer engagement makes the next one faster.

This is a production engineering role. The systems you build run live, get depended on, and are held to production standards for quality, provenance, and security.

THE PERSON:

You are equal parts data engineer and applied AI engineer. You think about agents as data systems: what context they retrieve, what memory they accumulate, what tools they invoke, and how you would prove they behave correctly. You have shipped pipelines that other teams depend on and LLM applications that real users hit, and you know the difference between a demo agent and one that survives production. You hold strong opinions about context engineering, memory store design, and evaluation - and you can defend them with data.

KEY RESPONSIBILITIES:
  • Build production agentic AI systems on AMD Instinct GPU infrastructure: agent orchestration, tool/function calling (including MCP-based integrations), skills frameworks, and streaming inference integration against ROCm-based serving stacks (vLLM, SGLang)
  • Design and operate the memory and context data layer for agentic applications: vector, graph, and relational stores, embedding pipelines, retrieval and context-engineering strategies, and the freshness, provenance, and access-control policies that govern them
  • Build the Applied AI team's engagement memory and fleet data infrastructure: pipelines that ingest deployment telemetry, incident histories, and field knowledge into structured, queryable, agent-consumable form
  • Develop and maintain the skills library: reusable, versioned, agent-executable encodings of deployment and operational expertise, with the testing and review gates required before agents or engineers rely on them
  • Build evaluation infrastructure for agentic systems: regression suites, LLM-as-judge pipelines, behavioral test harnesses, and production quality monitoring
  • Harden agentic systems against real-world failure modes, including prompt injection through retrieved context and memory stores, data poisoning, and tool-misuse paths
  • Create the reference architectures and open artifacts that make AMD the credible platform for agentic workloads, contributing upstream to the open-source agent, serving, and data ecosystem
  • Partner with customer-facing engineers on live engagements: your systems deploy into customer environments, and you support their production behavior
PREFERRED EXPERIENCE:
  • Deep software engineering experience with significant production data engineering: pipelines, storage systems, and data quality at scale (level flexible for exceptional candidates)
  • Hands-on experience building LLM-powered and agentic applications in production: agent frameworks and orchestration, RAG and context engineering, tool calling, and multi-step workflows
  • Depth in at least one memory/context storage paradigm - vector databases, graph databases, or hybrid retrieval architectures - and informed opinions about when each is wrong
  • Experience designing evaluation frameworks for non-deterministic systems
  • Strong Python; working fluency with modern data stack tooling (orchestration, streaming, warehouse/lakehouse) and containerized deployment on Kubernetes
  • Familiarity with GPU inference serving (vLLM, SGLang, or comparable) and the performance characteristics of LLM workloads; ROCm/AMD Instinct experience a strong plus
  • Security-conscious engineering instincts, particularly around untrusted content flowing into model context
  • Open-source contribution history in the AI/ML or data infrastructure ecosystem is a plus
PREFERRED ACADEMIC CREDENTIALS:
  • Bachelor's or Master's degree in Computer Science, Computer Engineering, Data Engineering, or equivalent practical experience

This role is not eligible for visa sponsorship.

Benefits offered are described: AMD benefits at a glance.

AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.

AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD's "Responsible AI Policy" is available here.

This posting is for an existing vacancy.

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