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Databricks Lakehouse Jobs (NOW HIRING)

Onebridge

Databricks Practice Lead

Indianapolis, IN · On-site

You bring deep expertise in the Databricks Lakehouse Platform and its evolving ecosystem, offering clarity to complex challenges including migrations, governance, and AI enablement. You combine ...

Tror AI for everyone

Databricks Architect

Los Angeles, CA · On-site

$69.75 - $91.50/hr

Design end-to-end Databricks Lakehouse architectures for data ingestion, processing, storage, and consumption. * Define and implement Delta Lake patterns, including medallion architecture (Bronze ...

Onebridge

Databricks Practice Lead

Indianapolis, IN · On-site +1

You bring deep expertise in the Databricks Lakehouse Platform and its evolving ecosystem, offering clarity to complex challenges including migrations, governance, and AI enablement. You combine ...

Onebridge

Databricks Practice Lead

Indianapolis, IN

You bring deep expertise in the Databricks Lakehouse Platform and its evolving ecosystem, offering clarity to complex challenges including migrations, governance, and AI enablement. You combine ...

TMS

Databricks Architect

Troy, MI · Remote

$66.25 - $87/hr

Role Overview We are looking for a Databricks Architect to design and lead modern Lakehouse data platforms using Databricks. The role focuses on building scalable, high-performance data pipelines and ...

SDH Systems

Sr Databricks Engineer

San Diego, CA

$110K - $152K/yr

The ideal candidate will have strong expertise in the Databricks Lakehouse platform, building and managing scalable data pipelines, working with notebooks, and implementing robust data monitoring ...

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Databricks Lakehouse information

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$10

$67

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How much do databricks lakehouse jobs pay per hour?

As of Jun 12, 2026, the average hourly pay for databricks lakehouse in the United States is $67.68, according to ZipRecruiter salary data. Most workers in this role earn between $23.32 and $92.31 per hour, depending on experience, location, and employer.

Does Databricks have unlimited PTO?

As a Databricks Lakehouse employee, the company's PTO policies vary by location and role, and unlimited PTO is not universally offered. Many companies in the tech industry provide flexible or unlimited PTO options, but it is best to review specific company policies or employment agreements for accurate details.

What are some common challenges Databricks Lakehouse professionals face when integrating data from multiple sources, and how can they be addressed?

A frequent challenge for Databricks Lakehouse professionals is handling data integration from diverse sources with varying formats, quality, and update frequencies. Addressing this requires designing robust ETL pipelines, leveraging Delta Lake for data reliability, and employing tools like Auto Loader for scalable ingestion. Collaboration with data engineering and analytics teams is also key to ensure consistent data models and governance. Staying updated with platform features and best practices can significantly streamline these integration efforts.

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

To thrive as a Databricks Lakehouse Engineer, you need a solid background in data engineering, cloud computing (especially Azure or AWS), and proficiency with Spark, Python, or Scala. Experience with Databricks platform tools, Delta Lake, SQL, and certifications like Databricks Certified Data Engineer are highly valuable. Strong problem-solving skills, collaboration, and effective communication set exceptional professionals apart in this role. These abilities are crucial for building scalable data solutions, ensuring data quality, and driving business insights in complex data environments.

What are lakeflow jobs in Databricks?

Lakeflow jobs in Databricks refer to automated data processing tasks that run on the Lakehouse platform, enabling data engineers and analysts to orchestrate data pipelines efficiently. These jobs typically involve scheduling, monitoring, and managing workflows using Databricks tools like Jobs API or Databricks Workflows, often requiring knowledge of Spark, SQL, and cloud environments.

Is Databricks a lakehouse or warehouse?

Databricks is a unified data platform that primarily implements a lakehouse architecture, combining the scalability of data lakes with the management features of data warehouses. It enables data engineers and analysts to perform large-scale data processing, analytics, and machine learning within a single environment. While it supports data warehousing functionalities, its core design is centered around the lakehouse model.

Are Databricks high in demand?

Databricks Lakehouse professionals are in high demand due to the growing need for data engineering, analytics, and machine learning skills in organizations adopting cloud-based data platforms. Expertise in Spark, SQL, and cloud environments like AWS or Azure enhances job prospects in this field.

What is a Databricks Lakehouse?

A Databricks Lakehouse is a unified data platform that combines the best features of data lakes and data warehouses. It allows organizations to store all their data—structured, semi-structured, and unstructured—in one place, while providing tools for data engineering, analytics, machine learning, and business intelligence. The Lakehouse architecture simplifies data management, reduces costs, and improves collaboration across teams by eliminating data silos. Databricks Lakehouse is built on open standards and supports a wide range of data processing workloads, making it an ideal solution for modern data-driven enterprises.
Infographic showing various Databricks Lakehouse job openings in the United States as of June 2026, with employment types broken down into 91% Full Time, 3% Part Time, and 6% Contract. Highlights an 81% Physical, 5% Hybrid, and 14% Remote job distribution, with an average salary of $140,766 per year, or $67.7 per hour.

Principal Observability Architect (Splunk & Databricks)

Scicom Infrastructure Services, Inc.

Atlanta, GA

Other

Posted 8 days ago

Job description

Position Summary


We are seeking a highly experienced Principal Observability Architect to lead the design, implementation, modernization, and optimization of enterprise-scale observability and analytics platforms. This role will serve as the technical authority for log management, observability engineering, telemetry pipelines, AIOps, security analytics, and data lakehouse architectures leveraging Splunk, Databricks, Cribl, OpenTelemetry, and cloud-native technologies.

The ideal candidate possesses deep expertise in traditional observability platforms (Splunk, Dynatrace, AppDynamics, ServiceNow ITOM) and modern data lakehouse architectures utilizing Databricks, Delta Lake, Unity Catalog, and AI/ML-driven analytics. This individual will drive the strategic transformation from legacy SIEM and observability platforms toward scalable, cloud-native observability data lakes.


Key Responsibilities


Enterprise Architecture & Strategy

  • Define enterprise observability architecture standards, patterns, and roadmaps.
  • Lead observability transformation initiatives involving Splunk modernization and Databricks adoption.
  • Develop reference architectures for telemetry ingestion, storage, analytics, security, and AI-driven operations.
  • Align observability strategies with business, security, compliance, and operational objectives.
  • Create executive-level architecture presentations, business cases, and technology roadmaps.


Splunk Platform Leadership

  • Architect large-scale Splunk Enterprise and Splunk Cloud environments.
  • Design and optimize:
    • Indexer clusters
    • Search head clusters
    • Forwarder architectures
    • Deployment servers
    • Data models
    • ITSI implementations
  • Define ingestion, retention, indexing, and data lifecycle strategies.
  • Lead migration initiatives involving:
    • Splunk to Databricks
    • Heavy Forwarders to Cribl
    • SIEM modernization programs
  • Optimize SPL searches, data models, summary indexing, and dashboard performance.


Databricks & Lakehouse Architecture

  • Architect enterprise observability data lake solutions using:
    • Databricks Lakehouse
    • Delta Lake
    • Unity Catalog
    • Delta Live Tables
    • Structured Streaming
    • Mosaic AI
    • Genie
  • Design Medallion Architectures:
    • Bronze
    • Silver
    • Gold
  • Develop governance strategies including:
    • RBAC
    • Data masking
    • Data lineage
    • Audit controls
  • Create high-performance log analytics solutions capable of supporting petabyte-scale telemetry environments.
  • Enable self-service analytics and AI-powered observability use cases.


Telemetry & Data Engineering

  • Design ingestion architectures supporting:
    • OpenTelemetry
    • OCSF
    • Syslog
    • Kafka
    • Azure Event Hubs
    • AWS Kinesis
    • GCP Pub/Sub
    • Cribl
  • Define normalization and enrichment frameworks.
  • Establish data quality and schema management processes.
  • Design real-time and batch processing pipelines.


AIOps & Advanced Analytics

  • Lead implementation of:
    • AIOps
    • Predictive analytics
    • Root cause analysis
    • Anomaly detection
    • Event correlation
  • Integrate observability datasets with AI/ML platforms.
  • Develop observability use cases leveraging:
    • Mosaic AI
    • Agentic AI
    • LLMs
    • Generative AI
  • Build operational intelligence and executive KPI dashboards.


Security & Compliance

  • Architect observability solutions supporting:
    • SOC operations
    • Threat hunting
    • Security analytics
    • Compliance reporting
  • Design frameworks aligned with:
    • HIPAA
    • PCI-DSS
    • SOX
    • NIST
    • ISO 27001
  • Implement data governance and security controls across observability platforms.


Leadership & Governance

  • Provide technical leadership to engineering teams.
  • Mentor architects, engineers, and developers.
  • Conduct architecture reviews and design governance.
  • Define platform standards, best practices, and operational procedures.
  • Engage directly with executive stakeholders and business leaders.


Required Qualifications


Experience

  • 10+ years of experience in Enterprise Observability, Monitoring, or Security Analytics.
  • 5+ years architecting large-scale Splunk environments.
  • 3+ years designing Databricks Lakehouse architectures.
  • Experience managing environments exceeding:
    • 50 TB/day preferred
    • 100+ TB/day strongly preferred
  • Experience leading enterprise transformation programs.


Splunk Expertise

Deep expertise in:

  • Splunk Enterprise
  • Splunk Cloud
  • Splunk ITSI
  • Enterprise Security
  • SPL Development
  • Data Models
  • Indexer Clustering
  • Search Head Clustering
  • SmartStore
  • Heavy Forwarders
  • Universal Forwarders

Databricks Expertise

Strong experience with:

  • Databricks Lakehouse
  • Delta Lake
  • Unity Catalog
  • Delta Live Tables
  • Structured Streaming
  • Databricks SQL
  • Genie
  • Mosaic AI
  • Lakehouse Federation

Cloud Platforms

Experience with one or more:

  • Microsoft Azure
  • Amazon Web Services
  • Google Cloud

Data Technologies

Strong knowledge of:

  • Kafka
  • OpenTelemetry
  • OCSF
  • Iceberg
  • Spark
  • SQL
  • Python
  • REST APIs
  • Event Streaming Architectures


Preferred Qualifications

  • Experience with Cribl Stream and Cribl Edge
  • Experience with Dynatrace, AppDynamics, Datadog, or New Relic
  • Experience with ServiceNow ITOM/Event Management
  • Experience designing AI/ML operational analytics solutions
  • Experience with Security Data Lakes and SIEM modernization initiatives
  • Experience with FinOps and cloud cost optimization
  • Experience building observability platforms for healthcare, financial services, retail, or large enterprise organizations


Certifications (Preferred)

Splunk

  • Splunk Enterprise Certified Architect
  • Splunk Core Certified Consultant

Databricks

  • Databricks Certified Data Engineer Professional
  • Databricks Certified Solutions Architect

Cloud

  • Azure Solutions Architect Expert
  • AWS Solutions Architect Professional
  • Google Professional Cloud Architect


Success Metrics

Within the first 12 months, the architect will:

  • Deliver enterprise observability architecture roadmap.
  • Reduce observability platform costs through modernization initiatives.
  • Design and implement a scalable observability data lake architecture.
  • Improve telemetry ingestion performance and reliability.
  • Enable AI-powered analytics and operational intelligence capabilities.
  • Establish enterprise governance standards for observability and security telemetry.
  • Support petabyte-scale observability and security analytics workloads.


Ideal Background

Candidates from organizations utilizing large-scale observability environments such as healthcare, banking, retail, telecommunications, logistics, cloud providers, or managed services organizations are highly desirable. Experience supporting environments generating 100TB+ of telemetry per day and integrating Splunk, Databricks, Cribl, OpenTelemetry, and cloud-native data platforms is strongly preferred.

Apply