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Data Stacks Jobs in Nevada (NOW HIRING)

Data & Software Engineer

Las Vegas, NV · On-site

$104K - $125K/yr

... stack experience). * Proven ability to build and operate automated, production-grade data pipelines and models (ELT/ETL, validation, monitoring). * Solid understanding of system integration ...

Data & Software Engineer

Las Vegas, NV · On-site

$104K - $125K/yr

... stack experience). * Proven ability to build and operate automated, production-grade data pipelines and models (ELT/ETL, validation, monitoring). * Solid understanding of system integration ...

A Data Center Technician is responsible for supporting the installation, maintenance, and ... This role focuses on structured cabling, rack and stack, equipment installation, and maintaining ...

A Data Center Technician you will handle hardware installations, troubleshooting, and maintenance ... This includes tasks like rack and stack equipment, cable management, hardware diagnostics, and ...

A Data Center Technician you will handle hardware installations, troubleshooting, and maintenance ... This includes tasks like rack and stack equipment, cable management, hardware diagnostics, and ...

A Data Center Technician is responsible for supporting the installation, maintenance, and ... This role focuses on structured cabling, rack and stack, equipment installation, and maintaining ...

Conduct physical inspections as requested or audits of servers, components Overview 1. Data Center Operations Perform rack & stack of servers, network devices, and storage equipment Handle cabling ...

Data Center Technician Reno, NV What are the top 3 skills required for this role? 1.Install Servers ... stack, and cable servers or switches to enable remote access by network administrators for ...

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Data Center Technician (L1)

Reno, NV · On-site

$19 - $21/hr

Data Center Field Installations Technician (L1) Location: Reno, NV (Onsite - Shift Work) Level ... Rack and stack servers, storage, and network equipment per design specifications * Perform ...

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Data Center Technician (L1)

Reno, NV · On-site

$19 - $21/hr

Data Center Field Installations Technician (L1) Location: Reno, NV (Onsite - Shift Work) Level ... Rack and stack servers, storage, and network equipment per design specifications * Perform ...

Be Seen First

Data Center Field Installations Technician (L1) Location: Reno, NV (Onsite - Shift Work) Level ... Rack and stack servers, storage, and network equipment per design specifications * Perform ...

New

Perform rack & stack of servers, network devices, and storage equipment * Handle cabling (fiber ... Follow data center security and access protocols * Adhere to ESD, safety, and PPE standards

Rack, stack, and cable servers, switches, and storage devices. Network troubleshooting: Diagnose ... Data Center, Network/Server troubleshooting and hardware break-fix experience Good experience in ...

Perform rack & stack of servers, network devices, and storage equipment * Handle cabling (fiber ... Follow data center security and access protocols * Adhere to ESD, safety, and PPE standards

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Data Stacks information

What are Data Stacks?

Data stacks refer to the combination of tools, technologies, and frameworks used to collect, store, process, and analyze data within an organization. A typical data stack might include data ingestion tools, databases, data warehouses, processing frameworks, and analytics platforms. The purpose of a data stack is to streamline the flow of data from its source to actionable insights, supporting business intelligence and decision-making. Common examples include the Modern Data Stack, which often uses tools like Fivetran, Snowflake, dbt, and Looker.

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

To thrive as a Data Engineer, you need strong skills in database design, data modeling, and programming languages like Python or Java, typically supported by a degree in computer science or a related field. Familiarity with tools such as SQL, Apache Spark, Hadoop, and cloud platforms like AWS or Google Cloud, as well as certifications in these technologies, is highly valued. Problem-solving abilities, attention to detail, and effective communication are important soft skills for this role. These skills are crucial for building reliable data pipelines, ensuring data quality, and supporting data-driven decision-making within organizations.

What is the difference between Data Stacks vs Data Analysts?

AspectData StacksData Analysts
Required CredentialsBachelor's in Computer Science, Data Science, or related fields; certifications like SQL, Python, or cloud platformsBachelor's in Statistics, Mathematics, or related fields; often certifications in Excel, SQL, or data visualization tools
Work EnvironmentTechnical teams, data engineering, software development environmentsBusiness units, reporting teams, data visualization platforms
Employer & Industry UsageTech companies, data-driven organizations, startupsFinance, marketing, healthcare, consulting firms

Data Stacks focus on building and managing the underlying data infrastructure, while Data Analysts interpret data to provide insights. Both roles require analytical skills, but Data Stacks professionals are more technical, working with data pipelines and databases, whereas Data Analysts focus on analyzing data to support decision-making.

What are some common challenges faced when managing modern data stacks, and how can they be addressed in this role?

Professionals managing modern data stacks often encounter challenges such as integrating diverse data sources, ensuring data quality, and maintaining system scalability as data volumes grow. Addressing these challenges typically involves collaborating closely with data engineers, analysts, and IT teams to implement robust data pipelines, automate data validation, and monitor system performance. Staying updated with evolving technologies and best practices is also crucial for proactive problem-solving and optimizing the data stack for business needs.
Data & Software Engineer

$104K - $125K/yr

Other

Posted 13 hours ago


Job description

Description

Overview:


The Data & Software Engineer will be responsible for the company's data foundation end to end, including integrations, data quality, data modeling, governance enablement, and analytics delivery. This is a cross-functional role with enterprise-wide responsibility for data and serves as the subject matter expert for the standardized unified data platform. The role partners with data product owners to ensure data is entered and maintained consistently and enables teams to produce reliable, automatically updating dashboards and reports.


Responsibilities:

Data Platform Ownership

  • Design, build, operate, and maintain our Microsoft data platform: Fabric / OneLake / Power BI / Azure components as appropriate.
  • Establish automated patterns for ingestion, transformation, modeling, security, monitoring, and cost management.
  • Create and maintain semantic models that support consistent dashboarding and reporting across the business and enable teams to use them effectively.

Software Engineering & Integrations

  • Integrate data across multiple systems using APIs, files, databases, middleware/iPaaS, and approaches for systems without APIs.
  • Build and maintain reliable ingestion pipelines (batch and/or near real-time where needed).
  • Write and maintain production-quality code to support pipelines, transformations, integrations, and internal tooling.
  • Develop supporting tools/services where needed to automate workflows and improve data reliability.

Data Quality, Standards & Governance Enablement

  • Define practical data standards (definitions, naming, required fields, validation rules, ownership, etc.).
  • Partner with business stakeholders and data product owners to improve upstream data entry processes and reduce downstream cleanup.
  • Implement data validation, reconciliation, and lineage so stakeholders can trust the data.


Power BI Enablement & Reporting

  • Build (and enable others to build) core dashboards and reporting patterns in Power BI.
  • Train and guide stakeholders on dashboarding best practices, KPI definitions, and self-service reporting within guardrails.
  • Create reusable templates/standards for metrics, visuals, and report structure.

Data Strategy & Continuous Improvement

  • Serve as the subject matter expert for enterprise data.
  • Think proactively about Blue Heron's organizational data strategy - prioritizing what matters most, aligning stakeholders, and adapting as systems/processes evolve.
  • Identify opportunities to improve data quality, automation, and reporting maturity in a constantly evolving environment.

Future-State

  • Build internal applications/tools (including custom proprietary platforms for Blue Heron).
  • Prototype and deliver AI-enabled apps/workflows (e.g., copilots, RAG, intelligent search, automated insights).


Requirements

Essential Qualifications:

  • Bachelor's degree in Computer Science or a related field (or equivalent practical experience).
  • 5+ years building data platforms and analytics solutions using Microsoft technologies such as Power BI, Azure Data Factory/SSIS, Azure Synapse, and Azure Data Lake/SQL (or equivalent modern data stack experience).
  • Proven ability to build and operate automated, production-grade data pipelines and models (ELT/ETL, validation, monitoring).
  • Solid understanding of system integration, including REST APIs and approaches for systems without APIs (vendor exports, file drops, DB connections, etc.).
  • Ability to code in relevant languages for data engineering and software development (e.g., Python, SQL, C#, JavaScript/TypeScript or similar).
  • Experience improving data quality by working upstream with business teams.
  • Ability to communicate clearly with technical and non-technical stakeholders; comfortable training users and setting standards.
  • High-ownership mindset: you build it, you own it, you improve it.

Preferred Qualifications:

  • Direct experience with Microsoft Fabric (Lakehouse/Warehouse, pipelines, notebooks, semantic models, governance).
  • Application development experience (internal tools, lightweight web apps, workflows).
  • Experience using AI-assisted coding tools productively (accelerating delivery while maintaining code quality).
  • AI app building experience (Azure OpenAI, copilots, RAG patterns, orchestration, etc.).
  • Experience in construction/homebuilding or project-based services data.