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Dataops Jobs (NOW HIRING)

Greystar Worldwide, LLC

DataOps Engineer

$99K - $136K/yr

SUMMARY Greystar is seeking a DataOps Engineer to join the Data Marketplace (DMP) team. This is a deeply technical, hands-on platform engineering role at the core of Greystar's enterprise data ...

Trioptus

DataOps Engineer

Montpelier, VT · On-site

DataOps Engineer Job Type: Contract Duration: 12-15 months (with potential for extension) Work Location: Remote (U.S.-based) Work Hours: Standard business hours Job Overview We are seeking an ...

TriOptus LLC

DataOps Engineer

Montpelier, VT · On-site

$115K - $139K/yr

DataOps Engineer Job Type: Contract Duration: 12-15 months (with potential for extension) Work Location: Remote (U.S.-based) Work Hours: Standard business hours Job Overview We are seeking an ...

Trioptus

DataOps Engineer

Montpelier, VT · On-site

DataOps Engineer Job Type: Contract Duration: 12 15 months (with potential for extension) Work Location: Remote (U.S.-based) Work Hours: Standard business hours Job Overview We are seeking an ...

Lorven Technologies

DataOps & Build Engineer

DataOps & Build Engineer will lead the architecture and optimization of a next-generation data platform. This critical role requires expertise to drive technical direction, mentor teams, and automate ...

Panopto

DataOps Engineer

$140K - $150K/yr

As we scale our global reach, we are seeking a DataOps Engineer to architect the 'Golden Path' for our data infrastructure. You will transform how we manage the data powering Panopto for 10+ million ...

Panopto

DataOps Engineer

OR · Remote

$140K - $150K/yr

As we scale our global reach, we are seeking a DataOps Engineer to architect the 'Golden Path' for our data infrastructure. You will transform how we manage the data powering Panopto for 10+ million ...

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How much do dataops jobs pay per hour?

As of Jun 24, 2026, the average hourly pay for dataops in the United States is $23.13, according to ZipRecruiter salary data. Most workers in this role earn between $17.55 and $24.04 per hour, depending on experience, location, and employer.

What are DataOps?

DataOps, short for Data Operations, is a set of practices, processes, and technologies that combine data engineering, data integration, and DevOps methodologies to improve the quality and speed of data analytics. DataOps aims to streamline the flow of data from source to value, enabling organizations to deliver reliable, high-quality data to stakeholders more efficiently. This approach emphasizes collaboration, automation, and monitoring throughout the data lifecycle to reduce errors and shorten development cycles. The ultimate goal of DataOps is to create an agile data pipeline that adapts quickly to changing business needs.

What jobs in the US pay 300,000 a year?

In the US, senior DataOps roles such as Lead Data Engineer or Director of Data Engineering can reach or exceed a $300,000 annual salary, especially with extensive experience, advanced skills in cloud platforms, automation, and data pipeline management. High compensation is often associated with leadership positions, specialized expertise, and working in large organizations or tech companies.

Is DataOps a good career?

DataOps is a growing field focused on streamlining data management and automation using tools like CI/CD pipelines and cloud platforms. It offers opportunities for professionals with skills in data engineering, scripting, and analytics, and typically involves collaborative work in fast-paced environments. The role can be rewarding for those interested in data workflows and continuous improvement processes.

What is the difference between Dataops vs Data Engineer?

AspectDataopsData Engineer
Primary FocusAutomating data workflows, deployment, and operational efficiencyBuilding and maintaining data pipelines, storage, and infrastructure
Skills & CertificationsDevOps tools, scripting, cloud platforms, CI/CD practicesSQL, ETL tools, cloud platforms, programming (Python, Scala)
Work EnvironmentCollaborates with DevOps, data teams, and operationsWorks closely with data scientists, analysts, and infrastructure teams
Industry UsageUsed in organizations focusing on data deployment and automationUsed in data infrastructure development and data pipeline creation

While both Dataops and Data Engineers work with data infrastructure, Dataops emphasizes automation, deployment, and operational efficiency, whereas Data Engineers focus on building and maintaining data pipelines and storage systems. Understanding these differences helps organizations assign the right roles for their data needs.

What jobs pay 500,000 a year in the US?

High-paying roles such as senior executives, specialized surgeons, and successful entrepreneurs can earn $500,000 or more annually. In the tech industry, senior data professionals like DataOps engineers with extensive experience, advanced skills in automation and cloud platforms, and leadership responsibilities may also reach this income level, especially in large organizations or consulting firms. Achieving this salary typically requires significant expertise, certifications, and years of experience.

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

To thrive as a DataOps Engineer, you need expertise in data engineering, automation, cloud platforms, and a solid understanding of CI/CD pipelines, typically backed by a degree in computer science or related fields. Familiarity with tools like Apache Airflow, Kubernetes, Docker, Jenkins, and cloud services such as AWS, GCP, or Azure is commonly required, along with knowledge of scripting languages like Python or Bash. Strong collaboration, problem-solving, and communication skills help DataOps professionals work effectively across data, development, and operations teams. These abilities ensure reliable, scalable, and efficient data infrastructure, enabling organizations to quickly deliver high-quality data solutions.

What jobs pay 200,000 a year in the USA?

DataOps professionals with extensive experience, advanced skills in data management, automation, and cloud platforms can earn salaries approaching or exceeding $200,000 annually, especially in senior or specialized roles. High-paying positions often require certifications, strong technical expertise, and leadership responsibilities within data engineering or analytics teams.

How does a DataOps professional typically collaborate with data engineers, analysts, and other IT teams?

DataOps professionals play a key role in bridging the gap between data engineering, analytics, and IT by facilitating efficient, automated workflows and ensuring data quality across the pipeline. They often work closely with data engineers to streamline data integration and deployment processes, while collaborating with analysts to support timely access to reliable data. Regular communication and cross-functional teamwork are essential, as DataOps is responsible for implementing best practices that help different teams deliver insights faster and with fewer errors. This collaborative environment also encourages continuous feedback and process improvement.
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Dataops jobs near you
Infographic showing various Dataops job openings in the United States as of June 2026, with employment types broken down into 87% Full Time, and 13% Contract. Highlights an 62% In-person, and 38% Remote job distribution, with an average salary of $48,110 per year, or $23.1 per hour.
DataOps Engineer

DataOps Engineer

Greystar Worldwide, LLC

Remote

$99K - $136K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 20 days ago

Greystar rating

7.9

Company rating: 7.9 out of 10

Based on 282 frontline employees who took The Breakroom Quiz

54th of 154 rated real estate companies

Job description

ABOUT GREYSTAR
Greystar is a leading, fully integrated global real estate platform offering expertise in property management, investment management, development, and construction services in institutional-quality rental housing. Headquartered in Charleston, South Carolina, Greystar manages and operates over $300 billion of real estate in more than 265 markets globally with offices throughout North America, Europe, South America, and the Asia-Pacific region. Greystar is the largest operator of apartments in the United States, managing over one million units/beds globally. Across its platforms, Greystar has nearly $79 billion of assets under management, including over $35 billion of development assets and over $36.5 billion of regulatory assets under management. Greystar was founded by Bob Faith in 1993 to become a provider of world-class service in the rental residential real estate business. To learn more, visit www.greystar.com.
JOB DESCRIPTION SUMMARY
Greystar is seeking a DataOps Engineer to join the Data Marketplace (DMP) team. This is a deeply technical, hands-on platform engineering role at the core of Greystar's enterprise data infrastructure - a Databricks-native medallion architecture (Bronze → Silver → Gold) running entirely on Microsoft Azure. You will own the reliability, scalability, and operational excellence of the DMP platform, working within DataOps pod inside the broader Analytics Engineering umbrella.
This role is Databricks and Azure-heavy. Most of your day lives inside Databricks - Delta Live Tables, Unity Catalog, Jobs, Workflows - backed by the full Azure data services stack including ADF, ADLS Gen2, Azure Monitor, Key Vault, and more. Deep mastery of both platforms is a baseline expectation, not a differentiator.
Critically, we expect this engineer to use AI as a first-class tool in their DataOps and observability practice - today, not eventually. That means AI-driven pipeline diagnostics, LLM-assisted root cause analysis, intelligent anomaly detection, and agentic observability agents that surface issues before they reach production. If you are still approaching DataOps the same way you did three years ago, this is not the right role. We are building self-aware, self-healing data infrastructure and need an engineer who is already operating that way.
You will also own the full deployment lifecycle - promoting data pipeline changes and platform configurations across dev, staging, and production environments using GitHub Enterprise and Linear for structured release management. Strong CI/CD discipline, environment promotion hygiene, and release coordination are as important here as pipeline engineering craft.
JOB DESCRIPTION
Key Responsibilities
AI-Driven DataOps & Observability
  • Implement AI-powered observability - using LLMs and ML models to detect pipeline drift, classify anomalies, predict SLA risk, and generate automated incident summaries
  • Build agentic monitoring workflows that proactively surface data quality degradation, pipeline dropout, schema drift, and volume anomalies across all DMP layers
  • Integrate AI tooling (Databricks Mosaic AI, Genie, OpenAI APIs, or equivalent) into operational DataOps processes - not as experiments, but as production-grade capabilities
  • Develop and maintain AI-assisted root cause analysis tooling to reduce MTTR on pipeline failures, with structured learnings fed back into the platform
  • Contribute to Greystar's 18-month agentic AI roadmap, leading near-term delivery of self-healing pipeline capabilities
Azure Infrastructure & Integration
  • Operate the full Azure data services stack supporting DMP: ADLS Gen2, Azure Data Factory (ADF), Azure Monitor, Log Analytics, Key Vault, and Event Hub
  • Design and maintain ADF pipelines for source system ingestion, including orchestration patterns for multi-tenant ERP environments (Yardi, Entrata, RealPage)
  • Collaborate with Azure infrastructure and cloud engineering teams on networking, identity, security, and resource provisioning
  • Drive cost governance through Azure Cost Management, Databricks DBU optimization, and storage lifecycle policies
Databricks Platform Engineering
  • Own the design, build, and optimization of data pipelines on Databricks using Delta Live Tables (DLT), PySpark, Workflows, and Jobs across the full DMP medallion stack
  • Administer and govern the Databricks workspace: Unity Catalog, cluster policies, access controls, compute configurations, and Delta table lifecycle management
  • Tune Spark jobs for performance, reliability, and cost - profiling bottlenecks, optimizing partitioning, managing Z-ordering, and controlling compute spend
  • Leverage Databricks Mosaic AI and Genie to build AI-native DataOps capabilities including intelligent pipeline monitoring, anomaly detection, and natural language data access
  • Architect and enforce DMP platform standards: naming conventions, schema evolution policies, SLA tiers, and medallion layer contracts
CI/CD & Environment Deployments
  • Own the full deployment pipeline for DMP data workflows - promoting changes from development through staging to production with rigor and minimal disruption
  • Build and maintain CI/CD workflows using GitHub Enterprise, including branch strategies, pull request automation, environment-specific configuration management, and release gating
  • Use Linear for sprint planning, release tracking, and issue management across deployment cycles; coordinate engineering work items with cross-functional stakeholders
  • Enforce deployment standards: automated testing gates, rollback procedures, change documentation, and environment parity controls
  • Partner with the analytics engineering and integration teams to align deployment cadences across the DMP stack

Data Quality & Governance
  • Instrument DQ checks across Bronze, Silver, and Gold layers covering completeness, consistency, accuracy, uniqueness, and referential integrity
  • Partner with Brett Finley's Data Governance team to enforce data contracts, ownership standards, and quality SLAs within Unity Catalog
  • Build feedback loops between DQ scoring, pipeline observability, and upstream source owners to drive systemic data reliability improvements

Collaboration & Documentation
  • Partner with analytics engineers, data governance, and product stakeholders to align pipeline and platform design with business requirements
  • Produce thorough technical documentation - runbooks, deployment playbooks, incident post-mortems, ADRs, and platform specs
  • Participate in on-call rotation and support SLA commitments for business-critical DMP data domains

Qualifications
Required
  • 7+ years of DataOps, data engineering, or platform engineering experience in a production environment
  • Expert-level hands-on experience with Databricks: Delta Live Tables, Jobs/Workflows, Unity Catalog, Spark performance tuning, and Delta Lake internals
  • Strong command of the Azure data services ecosystem: ADF, ADLS Gen2, Azure Monitor, Log Analytics, Key Vault, and related services
  • Demonstrated, production use of AI tools in DataOps or data observability workflows - LLM-assisted diagnostics, intelligent alerting, agentic monitoring, or equivalent
  • Proven CI/CD experience using GitHub Enterprise - branch strategies, PR automation, environment promotion, and release management for data pipelines
  • Solid Python and/or Scala skills for pipeline development; SQL fluency for Gold layer transformation and DQ validation
  • Hands-on experience with ADF pipeline design and orchestration at scale
  • Experience with medallion / lakehouse architecture patterns and multi-environment deployment discipline
  • Strong collaborative skills across engineering, governance, and business stakeholder teams

Preferred
  • Experience with Linear for engineering sprint management and release tracking
  • Familiarity with Databricks Mosaic AI, Genie, or other AI-native Databricks capabilities
  • Exposure to agentic AI frameworks or MCP (Model Context Protocol) server integrations
  • Background in real estate, property management, or multi-source ERP data environments (Yardi, Entrata, RealPage)
  • Experience with Cosmos DB, Azure SQL, or similar operational data stores alongside lakehouse platforms
  • Knowledge of data governance frameworks, data lineage tooling, and metadata management within Unity Catalog
  • Background in legacy BI migration or platform modernization programs

What We Offer
  • A high-impact role at the center of Greystar's enterprise data transformation
  • Collaborative, engineering-driven team culture with a strong focus on craft, automation, and continuous improvement
  • Access to cutting-edge tooling - Databricks, full Azure stack, GitHub Enterprise, and an active AI innovation agenda
  • Competitive compensation, comprehensive benefits, and flexible work arrangements
  • Opportunity to define the DataOps discipline and lead Greystar's self-healing pipeline and agentic AI roadmap

The salary range for this position is $120,000 - $150,000 USD Annually.
#LI-BB1
#LI-Remote
Additional Compensation:
Many factors go into determining employee pay within the posted range including business requirements, prior experience, current skills and geographical location.
  • Corporate Positions: In addition to the base salary, this role may be eligible to participate in a quarterly or annual bonus program based on individual and company performance.
  • Onsite Property Positions: In addition to the base salary, this role may be eligible to participate in weekly, monthly, and/or quarterly bonus programs.

Robust Benefits Offered*:
  • Competitive Medical, Dental, Vision, and Disability & Life insurance benefits. Low (free basic) employee Medical costs for employee-only coverage; costs discounted after 3 and 5 years of service.
  • Generous Paid Time off. All new hires start with 15 days of vacation, 4 personal days, 10 sick days, and 11 paid holidays. Plus your birthday off after 1 year of service! Additional vacation accrued with tenure.
  • For onsite team members, onsite housing discount at Greystar-managed communities are available subject to discount and unit availability.
  • 6-Week Paid Sabbatical after 10 years of service (and every 5 years thereafter).
  • 401(k) with Company Match up to 6% of pay after 6 months of service.
  • Paid Parental Leave and lifetime Fertility Benefit reimbursement up to $10,000 (includes adoption or surrogacy).
  • Employee Assistance Program.
  • Critical Illness, Accident, Hospital Indemnity, Pet Insurance and Legal Plans.
  • Charitable giving program and benefits.

*Benefits offered for full-time employees. For Union and Prevailing Wage roles, compensation and benefits may vary from the listed information above due to Collective Bargaining Agreements and/or local governing authority.
Greystar will consider for employment qualified applicants with arrest and conviction records.
This position may be performed remotely anywhere within the United States except the state of Alaska.
Important Notice: Greystar will never request your banking details or other sensitive personal information during the interview process. Greystar does not conduct any interviews via text or messaging, and all communication will come from official Greystar email addresses (@greystar.com). If you receive suspicious requests, please report them immediately to AskHR@greystar.com.
ANTICIPATED CLOSING DATE
August 24, 2026
This date may be subject to change due to evolving business needs.

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