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Data Infrastructure Analyst Jobs in Indiana (NOW HIRING)

RADcube

Pharma Data Engineer - Databricks AWS

Indianapolis, IN · On-site

$109K - $131K/yr

You will design, build, and govern the data infrastructure that powers analytics and reporting across the business, while also acting as a trusted technical partner to non-technical stakeholders. Key ...

Pwc

Data Engineer - Senior Manager

Indianapolis, IN

$124K - $280K/yr

In data engineering at PwC, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for developing and ...

HydroJug

Cloud data warehouse experience (BigQuery or equivalent) and ownership of analytics-adjacent infrastructure * Ecommerce, retail, DTC, or consumer-goods domain background * Experience standing up ...

POLYWOOD

Director | Data Engineering

Syracuse, IN · On-site

... infrastructure that powers enterprise decisions. * Growth Opportunities: Shape standards, architecture, and long-term data strategy. * Team Environment: Partner with business leaders and analytics ...

OSC Global

Data Reliability Specialist

Fort Wayne, IN · On-site

... and Analysts, as well as government Cyber POCs to ensure compliance to federal and local ... infrastructure * Prepares daily, weekly, and monthly reports detailing task and responsibility ...

PadSplit

$45K - $55K/yr

We're looking for a Senior Data Analyst to own analyses end to end, build durable reporting infrastructure, and help shape how the company measures its business. This person will work daily across ...

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Data Infrastructure Analyst information

What is a data infrastructure analyst?

Data Infrastructure Analysts are professionals who design, maintain, and optimize the systems and architecture that store, process, and manage an organization's data. They work with databases, data warehouses, and cloud storage solutions to ensure data is accessible, reliable, and secure. Their role often involves collaborating with data engineers, IT teams, and business analysts to support data-driven decision-making. Data Infrastructure Analysts also monitor system performance, troubleshoot issues, and recommend improvements to enhance data management processes.

What are the key skills and qualifications needed to thrive as a data infrastructure analyst?

To thrive as a Data Infrastructure Analyst, you need a solid background in database management, data modeling, and systems analysis, often supported by a degree in computer science or a related field. Familiarity with SQL, ETL tools, cloud platforms (such as AWS or Azure), and certifications like AWS Certified Data Analytics or Google Cloud Data Engineer are commonly required. Strong problem-solving abilities, attention to detail, and effective communication help analysts collaborate across technical and business teams. These skills are crucial for ensuring robust, scalable data systems that support organizational decision-making and operational efficiency.

What are some common challenges data infrastructure analysts face when managing large-scale data systems?

Data Infrastructure Analysts frequently encounter challenges such as ensuring data integrity across distributed systems, optimizing data pipelines for performance, and maintaining system scalability as data volumes grow. Balancing security requirements with accessibility, especially in environments with sensitive information, is also a key concern. Additionally, collaborating with data engineers, database administrators, and business teams to align infrastructure with organizational goals requires strong communication and adaptability.

What is the difference between Data Infrastructure Analyst vs Data Engineer?

AspectData Infrastructure AnalystData Engineer
Required CredentialsBachelor's in IT, Computer Science, or related field; certifications like Microsoft Certified Data AnalystBachelor's or higher in Computer Science, Software Engineering; certifications like AWS Certified Data Analytics
Work EnvironmentCorporate offices, data centers, cloud platformsDevelopment environments, cloud platforms, data pipelines
Employer & Industry UsageFinance, healthcare, retail, tech companiesTech firms, finance, e-commerce, large enterprises
Common Search & ComparisonYesYes

The Data Infrastructure Analyst focuses on maintaining and optimizing existing data systems, ensuring data accessibility and quality. In contrast, Data Engineers design, build, and implement data pipelines and infrastructure from scratch. Both roles require similar credentials and often work in overlapping environments, but their core responsibilities differ in scope and focus.

Data Infrastructure Analyst jobs near you

Director, Data Engineering

Michigan City, IN • On-site

Dwyer Instruments, Inc.
Industrial Automation Equipment Manufacturing • 501 - 1,000 employees
Learn more about Dwyer Instruments, Inc.external

Full-time

Posted 21 days ago

Job description

Job Type
Full-time
Description
We are seeking a visionary Director, Data Engineering to architect the "data set of the future." This role is not just about reporting; it is about building the scalable, AI-ready infrastructure that will fuel our next generation of manufacturing innovation. You will move the organization beyond traditional data warehousing to a robust Data Lakehouse architecture, ensuring our enterprise data-from shop floor to point-of-sale-is clean, real-time, and ready for advanced GenAI and predictive modeling.
The ideal candidate is a technologist who fluently bridges the gap between the plant floor and the front office. You will be responsible for integrating complex operational data with high-velocity sales and commercial data to create a unified ecosystem. By connecting factory efficiency directly to customer demand and market trends, you will enable us to pivot from reactive operations to a truly predictive enterprise.
Key Responsibilities:
  • Architecting the Future: Define and execute a data infrastructure roadmap centered on a Lakehouse architecture that integrates structured and unstructured data, enabling both real-time operational analytics and high-scale AI/ML workloads.
  • AI-Ready Foundation: Establish the data governance, cataloging, and lineage frameworks necessary to power secure, trusted AI models and Large Language Models (LLMs) across the enterprise.
  • Manufacturing Integration: Partner with OT and Engineering teams to ingest and operationalize IIoT and supply chain data, creating a unified data ecosystem that drives predictive maintenance and factory floor efficiency.
  • Modern Data Stack Leadership: Oversee the transition from legacy BI tools to modern, self-service analytics platforms, ensuring the organization has the agility to derive insights from the data lakehouse.
  • Data Ops & Governance: Lead the transition to MLOps and DataOps methodologies, ensuring data quality, security, and compliance in an increasingly automated environment.
  • Strategic Partnership: Collaborate with business unit leaders to identify and prioritize data products that drive measurable top-line growth or operational cost reductions.
  • Team Leadership: Build and mentor a high-performing team of data engineers, ML engineers, and data architects who are comfortable in both cloud-native environments and complex legacy manufacturing systems.

Requirements
Qualifications and Technical Requirements:
  • Strategic Experience: 15+ years in data strategy, architecture, and engineering, with at least 5 years in a leadership role driving organizational change.
  • 5+ years in a leadership role managing data & analytics teams.
  • Architecture Expertise: Demonstrated experience designing and deploying Lakehouse architectures (e.g., Databricks, Snowflake, or similar) at scale.
  • AI/ML Fluency: Proven experience operationalizing AI/ML models within an enterprise environment; deep understanding of data preparation for LLMs and generative AI.
  • Cloud Proficiency: Extensive experience with Azure (or equivalent cloud hyperscaler) data stacks (e.g., Synapse/Fabric, ADLS Gen2, Azure AI).
  • Tooling: Advanced proficiency in Python, Spark, and SQL; strong experience with CI/CD for data pipelines and infrastructure-as-code.
  • Education: Bachelor's or Master's degree in Computer Science, Data Engineering, or a related technical field.
  • Soft Skills: A "product manager" mindset for data; the ability to translate complex technical architectural debt into business-friendly value proposition

Essential/Preferred Skills:
  • Experience with data governance frameworks and tools.
  • Exposure to advanced analytics, data science, or machine learning initiatives.
  • Experience in manufacturing, industrial, or eCommerce environments preferred.

Work Conditions and Physical Requirements:
  • Ability to work in both office and manufacturing environments.
  • Availability to work outside of core business hours, including nights, weekends, and holidays when required for system upgrades or migrations.
  • Required to sit or stand for long periods of time.
  • The ability to lift 30-50 lbs without assistance.
  • Local and/or international travel will be required as needed (10-15%) including some extended stays on location for education or deployments. Must have a valid driver's license and Passport.

Salary Description
165,000

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