Hire the Best Databricks Platform Specialists

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Shantanu K.

Nagpur, India

$18/hr
5.0
7 jobs

I am a Data Architect with 5+ yearsโ€™ experience in data engineering in Healthcare, Retail and Banking domains. Worked on multiple data warehousing and reporting projects with open source tools such as Apache Spark, Apache Airflow, Apache Kafka, Apache Superset. I also have experiance in open building open source data warehouses. I am microsoft certified data engineer and Azure administarator. With 5+ years of expirance in Microsoft azure technologies such as Azure Data Factory, Azure Databricks, MS SQL, Azure Synapse etc. I am Microsoft, Databricks and GCP Certified Data Engineer and am currently working as a Data Architect at FulzTech.

  • Databricks Platform
  • Python
  • Java
  • SQL
  • Snowflake
  • Data Modeling
  • Microsoft Power BI
  • Data Warehousing & ETL Software
  • ETL
  • Microsoft Azure SQL Database
  • Machine Learning
  • Android
  • Angular
  • NoSQL Database
Priyanshu B.

Gurgaon, India

$35/hr
4.2
2 jobs

Data engineer, Hadoop, Java, python, AWS, azure, gcp. Ambari, Kafka, hbase, Cassandra, elastic, mysql, Aws step function, AWS Glue, CDK, GitHub copilot, GitHub, Redshift, AWS Lambda, pyspark, Databricks

  • Databricks Platform
  • Apache Hadoop
  • Apache Cassandra
  • Apache Spark
  • PySpark
  • Apache HBase
  • AWS Glue
  • Amazon Athena
  • Java
  • MySQL
  • Amazon Redshift
  • ETL
  • Qlik Sense
  • Scala
  • Elasticsearch
Sumit D.

Gurugram, India

$30/hr
5.0
1 jobs

I design and build scalable ETL/ELT pipelines and modern cloud data platforms that power reliable analytics and reporting. With expertise in Azure, GCP, Databricks, Snowflake, DBT, SQL and PySpark, I deliver efficient, cost-optimized data solutions that transform raw data into trusted, actionable insights.

  • Databricks Platform
  • Data Lake
  • PySpark
  • SQL
  • Python
  • Microsoft Azure SQL Database
  • Microsoft Azure Administration
  • ETL Pipeline
  • dbt
  • Apache Airflow
  • Google Cloud Platform
  • Snowflake
  • Data Warehousing & ETL Software
  • Data Warehousing
  • Apache Spark
Muhammad H.

Karachi, Pakistan

$10/hr
5.0
1 jobs

Slow pipelines, unreliable data, or a warehouse that breaks every time the source changes? I build data systems that don't. I'm a ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฒ๐—ฑ ๐—™๐—ฎ๐—ฏ๐—ฟ๐—ถ๐—ฐ ๐——๐—ฎ๐˜๐—ฎ ๐—˜๐—ป๐—ด๐—ถ๐—ป๐—ฒ๐—ฒ๐—ฟ with 5 years of experience delivering end-to-end data engineering and BI solutions. I currently work at Pakistan's largest payment gateway, a high volume fintech environment where ๐—ง๐—•-๐˜€๐—ฐ๐—ฎ๐—น๐—ฒ ๐˜๐—ฟ๐—ฎ๐—ป๐˜€๐—ฎ๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐—ฎ๐—น ๐—ฑ๐—ฎ๐˜๐—ฎ, strict governance, and zero tolerance for pipeline failures are the daily reality. My specialty is building systems that are ๐—ฎ๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐—ฒ๐—ฑ ๐—ฝ๐—ฟ๐—ผ๐—ฝ๐—ฒ๐—ฟ๐—น๐˜† ๐—ณ๐—ฟ๐—ผ๐—บ ๐˜๐—ต๐—ฒ ๐˜€๐˜๐—ฎ๐—ฟ๐˜ metadata-driven, layered, monitored, and built to scale. ๐—ช๐—›๐—”๐—ง ๐—œ ๐—•๐—จ๐—œ๐—Ÿ๐—— โœฆ ๐— ๐—ฒ๐˜๐—ฎ๐—ฑ๐—ฎ๐˜๐—ฎ-๐——๐—ฟ๐—ถ๐˜ƒ๐—ฒ๐—ป ๐—˜๐—ง๐—Ÿ/๐—˜๐—Ÿ๐—ง ๐—ฃ๐—ถ๐—ฝ๐—ฒ๐—น๐—ถ๐—ป๐—ฒ๐˜€ Control logic lives in configuration, not hardcoded. One framework handles dozens of sources with built-in logging, error handling, and restartability. Proven: reduced ETL runtime by ๐Ÿฏ๐Ÿด% on a production enterprise warehouse by eliminating redundant mapping layers. โœฆ ๐—˜๐—ป๐˜๐—ฒ๐—ฟ๐—ฝ๐—ฟ๐—ถ๐˜€๐—ฒ ๐——๐—ฎ๐˜๐—ฎ ๐—ช๐—ฎ๐—ฟ๐—ฒ๐—ต๐—ผ๐˜‚๐˜€๐—ฒ ๐——๐—ฒ๐˜€๐—ถ๐—ด๐—ป End-to-end warehouse design across ๐—ฆ๐˜๐—ฎ๐—ด๐—ถ๐—ป๐—ด โ†’ ๐—–๐—ผ๐—ฟ๐—ฒ โ†’ ๐—š๐—ผ๐—น๐—ฑ (Medallion Architecture), with star/snowflake schema modeling, incremental loading, duplicate handling, and structured audit logging baked in. โœฆ ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐—™๐—ฎ๐—ฏ๐—ฟ๐—ถ๐—ฐ ๐—ฆ๐—ผ๐—น๐˜‚๐˜๐—ถ๐—ผ๐—ป๐˜€ Lakehouse and Warehouse design on OneLake, Fabric Data Factory pipelines, semantic models with ๐—ฅ๐—ผ๐˜„-๐—Ÿ๐—ฒ๐˜ƒ๐—ฒ๐—น ๐—ฆ๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜† (๐—ฅ๐—Ÿ๐—ฆ), and report publishing as Fabric Apps for internal teams and external stakeholders. โœฆ ๐—”๐˜‡๐˜‚๐—ฟ๐—ฒ & ๐——๐—ฎ๐˜๐—ฎ๐—ฏ๐—ฟ๐—ถ๐—ฐ๐—ธ๐˜€ ๐—ฃ๐—ถ๐—ฝ๐—ฒ๐—น๐—ถ๐—ป๐—ฒ๐˜€ ADF orchestrated cloud pipelines and PySpark based distributed data processing on Databricks for large-scale, partitioned datasets. โœฆ ๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—•๐—œ & ๐—ฆ๐—ฆ๐—ฅ๐—ฆ ๐—ฅ๐—ฒ๐—ฝ๐—ผ๐—ฟ๐˜๐—ถ๐—ป๐—ด Semantic model design, DAX measures, drill-through dashboards, RLS enforcement, SSRS and Report Builder reports, and Fabric App deployment for enterprise stakeholders. โœฆ ๐—Ÿ๐—ฒ๐—ด๐—ฎ๐—ฐ๐˜† ๐— ๐—œ๐—ฆ ๐— ๐—ถ๐—ด๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป Migrated 20+ reports from legacy systems into a centralized, modern BI architecture without disrupting ongoing operations. ๐—ฅ๐—˜๐—–๐—˜๐—ก๐—ง ๐—ฅ๐—˜๐—ฆ๐—จ๐—Ÿ๐—ง๐—ฆ โ€ข Reduced ETL runtime by ๐Ÿฏ๐Ÿด% (4 hrs โ†’ 2.5 hrs) by optimizing metadata-driven SSIS pipelines โ€ข Built automated SFTP ingestion pipelines with archive logic to ensure ๐—ถ๐—ป๐—ฐ๐—ฟ๐—ฒ๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น, ๐—ฑ๐˜‚๐—ฝ๐—น๐—ถ๐—ฐ๐—ฎ๐˜๐—ฒ-๐—ณ๐—ฟ๐—ฒ๐—ฒ data loading โ€ข Delivered ๐—บ๐˜‚๐—น๐˜๐—ถ๐—ฝ๐—น๐—ฒ ๐—˜๐—ป๐˜๐—ฒ๐—ฟ๐—ฝ๐—ฟ๐—ถ๐˜€๐—ฒ ๐——๐—ฎ๐˜๐—ฎ ๐—ช๐—ฎ๐—ฟ๐—ฒ๐—ต๐—ผ๐˜‚๐˜€๐—ฒ๐˜€ supporting different business products across fintech, billing, and payments โ€ข Published ๐Ÿญ๐Ÿฑ+ ๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—•๐—œ ๐—ฟ๐—ฒ๐—ฝ๐—ผ๐—ฟ๐˜๐˜€ as Fabric Apps with Row Level Security for external stakeholders โ€ข Onboarded 10+ new source tables into a redesigned data warehouse while improving ETL performance and storage efficiency โ€ข Worked extensively with ๐—ง๐—•-๐˜€๐—ฐ๐—ฎ๐—น๐—ฒ ๐˜๐—ฟ๐—ฎ๐—ป๐˜€๐—ฎ๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐—ฎ๐—น ๐—ฑ๐—ฎ๐˜๐—ฎ in a high-volume payment processing environment. ๐—–๐—ข๐—ฅ๐—˜ ๐—ฆ๐—ง๐—”๐—–๐—ž ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐—™๐—ฎ๐—ฏ๐—ฟ๐—ถ๐—ฐ | ๐—”๐˜‡๐˜‚๐—ฟ๐—ฒ ๐——๐—ฎ๐˜๐—ฎ ๐—™๐—ฎ๐—ฐ๐˜๐—ผ๐—ฟ๐˜† | ๐—”๐˜‡๐˜‚๐—ฟ๐—ฒ ๐——๐—ฎ๐˜๐—ฎ๐—ฏ๐—ฟ๐—ถ๐—ฐ๐—ธ๐˜€ | ๐—ฃ๐˜†๐—ฆ๐—ฝ๐—ฎ๐—ฟ๐—ธ | ๐—”๐—ฝ๐—ฎ๐—ฐ๐—ต๐—ฒ ๐—ฆ๐—ฝ๐—ฎ๐—ฟ๐—ธ | ๐—ฆ๐—ฆ๐—œ๐—ฆ | ๐—ฆ๐—ค๐—Ÿ ๐—ฆ๐—ฒ๐—ฟ๐˜ƒ๐—ฒ๐—ฟ | ๐—ข๐—ฟ๐—ฎ๐—ฐ๐—น๐—ฒ | ๐—ฃ๐—ผ๐˜€๐˜๐—ด๐—ฟ๐—ฒ๐—ฆ๐—ค๐—Ÿ | ๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—•๐—œ | ๐—ฆ๐—ฆ๐—ฅ๐—ฆ | ๐—ง-๐—ฆ๐—ค๐—Ÿ | ๐—ฃ๐—Ÿ/๐—ฆ๐—ค๐—Ÿ | ๐——๐—ฎ๐˜๐—ฎ ๐—ช๐—ฎ๐—ฟ๐—ฒ๐—ต๐—ผ๐˜‚๐˜€๐—ถ๐—ป๐—ด | ๐— ๐—ฒ๐—ฑ๐—ฎ๐—น๐—น๐—ถ๐—ผ๐—ป ๐—”๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ | ๐—ฆ๐˜๐—ฎ๐—ฟ ๐—ฆ๐—ฐ๐—ต๐—ฒ๐—บ๐—ฎ | ๐—ฆ๐—ป๐—ผ๐˜„๐—ณ๐—น๐—ฎ๐—ธ๐—ฒ ๐—ฆ๐—ฐ๐—ต๐—ฒ๐—บ๐—ฎ | ๐—˜๐—ง๐—Ÿ/๐—˜๐—Ÿ๐—ง | ๐—Ÿ๐—ฎ๐—ธ๐—ฒ๐—ต๐—ผ๐˜‚๐˜€๐—ฒ ๐—•๐—˜๐—ฆ๐—ง-๐—™๐—œ๐—ง ๐—ฃ๐—ฅ๐—ข๐—๐—˜๐—–๐—ง๐—ฆ โ€ข Data warehouse or lakehouse design from scratch โ€ข ETL/ELT pipeline build, optimization, or troubleshooting โ€ข Microsoft Fabric or Azure migration from legacy on-prem systems โ€ข Power BI, SSRS, or Fabric App reporting solutions โ€ข SQL performance tuning, stored procedures, and indexing โ€ข Production pipeline monitoring, job scheduling, and failure resolution ๐—›๐—ข๐—ช ๐—œ ๐—ช๐—ข๐—ฅ๐—ž I understand your business process, data sources, and reporting needs first. Then I design a practical architecture, build clean and observable pipelines, validate the data, and deliver reporting ready models your team can actually trust with ๐—น๐—ผ๐—ด๐—ด๐—ถ๐—ป๐—ด, ๐—ฒ๐—ฟ๐—ฟ๐—ผ๐—ฟ ๐—ต๐—ฎ๐—ป๐—ฑ๐—น๐—ถ๐—ป๐—ด, and ๐—ท๐—ผ๐—ฏ ๐˜€๐—ฐ๐—ต๐—ฒ๐—ฑ๐˜‚๐—น๐—ถ๐—ป๐—ด built in from day one, not added as an afterthought. ๐Ÿ“ฉ ๐—ฆ๐—ฒ๐—ป๐—ฑ ๐—บ๐—ฒ ๐—ฎ ๐—บ๐—ฒ๐˜€๐˜€๐—ฎ๐—ด๐—ฒ ๐˜„๐—ถ๐˜๐—ต ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ฝ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜ ๐—ฑ๐—ฒ๐˜๐—ฎ๐—ถ๐—น๐˜€. ๐—œ ๐—ฟ๐—ฒ๐˜€๐—ฝ๐—ผ๐—ป๐—ฑ ๐—พ๐˜‚๐—ถ๐—ฐ๐—ธ๐—น๐˜† ๐—ฎ๐—ป๐—ฑ ๐˜„๐—ถ๐—น๐—น ๐—ผ๐˜‚๐˜๐—น๐—ถ๐—ป๐—ฒ ๐—ฎ ๐—ฐ๐—น๐—ฒ๐—ฎ๐—ฟ ๐—ฎ๐—ฝ๐—ฝ๐—ฟ๐—ผ๐—ฎ๐—ฐ๐—ต ๐—ณ๐—ผ๐—ฟ ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ฝ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜.

  • Databricks Platform
  • Data Engineering
  • ETL Pipeline
  • Microsoft Azure
  • Microsoft Power BI
  • Data Warehousing
  • Data Lake
  • SQL
  • Data Modeling
  • SQL Server Integration Services
  • SQL Server Reporting Services
  • Microsoft SQL Server
  • Oracle
  • Fabric
  • Database Development
  • PySpark
  • Business Intelligence
  • PostgreSQL
  • Microsoft Power BI Data Visualization
  • Big Data
Arpit K.

Chennai, India

$11/hr
5.0
2 jobs

๐Ÿš€ GCP Certified Data Engineer | Google Cloud Platform | Python | SQL | Airflow | GCP workflows | Bigquery | DataForm | Power BI With a solid foundation in GCP (Google Cloud Platform) and a certification in Data Engineering, I bring a wealth of expertise to the table. As a Senior Data Engineer with ~5 years of industry experience, including working in both product-based and service-based companies, I excel in leveraging cutting-edge technology to deliver innovative solutions. ๐Ÿ”น Skills Highlights: GCP Expertise: Proficient in leveraging the power of Google Cloud Platform for data engineering tasks, ensuring efficient and scalable solutions. Python & PySpark: Adept at harnessing the capabilities of Python and PySpark to manipulate and analyze large datasets, driving actionable insights. SQL Mastery: Experienced in crafting complex SQL queries and optimizing database performance for streamlined data operations. Airflow expertise: Experienced with Apache Airflow, building ETL pipelines, managing task dependencies, and deploying and monitoring workflows. Power BI & Alteryx: Skilled in creating visually compelling dashboards and automating workflows using Power BI and Alteryx, empowering stakeholders with actionable insights. Problem-Solving: Known for my analytical mindset and ability to tackle complex challenges head-on, I thrive in dynamic environments where innovative solutions are required

  • Databricks Platform
  • BigQuery
  • SQL
  • Python
  • Data Engineering
  • Google Cloud Platform
  • ETL Pipeline
  • Data Warehousing
  • Apache Spark
  • Apache Airflow
  • PySpark
  • Computer Vision
Rahat G.

Noida, India

$15/hr
5.0
8 jobs

Prudent Cloud Data Engineer having 7+ year of experience in developing, maintaining and automating Data Engineering solutions using Microsoft Azure cloud technologies and, using languages like Python and R. Solving business problems end -to- end. Agentic AI LLM Models Microsoft Fabric Power Automate Power Apps Power BI Azure Data Factory Databricks SQL

  • Databricks Platform
  • Microsoft Power BI
  • MySQL Programming
  • Microsoft Azure SQL Database
  • Azure App Service
  • Microsoft PowerApps
  • Python Script
  • Microsoft Power Automate
  • Microsoft Azure
  • Fabric
  • Databricks MLflow
  • PySpark

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Don't just take our word for it

What does a Databricks Platform specialist do?

A Databricks Platform specialist administers the underlying infrastructure and governance controls of a Databricks workspace rather than writing data transformation code. This role secures the environment by configuring network connectivity, managing user identities, and enforcing access policies through Unity Catalog. The specialist builds automated deployment pipelines to maintain consistent platform settings across development and production environments.

  • Configure account and workspace administration by assigning specific roles to users, service principals, and groups. You define permission boundaries that restrict administrative capabilities based on organizational governance policies. This setup ensures that only authorized personnel can modify critical platform settings or access sensitive data assets.
  • Manage Unity Catalog permissions and privileges for catalogs, schemas, and tables using SQL commands or the Databricks CLI. You grant or revoke object-level access to control who reads, writes, or modifies data structures. This work establishes a centralized governance layer that tracks data lineage and enforces security standards across all connected compute resources.
  • Implement network security architectures by setting up private connectivity options such as AWS PrivateLink. You configure firewall rules and virtual network peering to isolate Databricks workspaces from public internet traffic. These settings protect data in transit and ensure that cluster communication remains within approved cloud provider boundaries.
  • Automate platform deployment and resource management using the Databricks Terraform provider. You write infrastructure as code scripts that provision workspaces, clusters, and policy definitions through REST API calls. This approach eliminates manual configuration errors and allows teams to replicate secure environments quickly for new projects or regions.

How to hire a Databricks Platform specialist on Upwork

Step 1: Post a job

Define your infrastructure needs clearly to attract specialists who manage workspace governance and security. The Job Post Generator powered by Umaโ„ข, Upwork's Mindful AI drafts a complete post from a few sentences about your requirements. You can write a new post, update a saved draft, or reuse an existing post to start your search.

  • Specify that the freelancer must configure Unity Catalog permissions for catalogs, schemas, and tables using SQL or CLI tools.
  • Request experience with the Databricks Terraform provider to automate platform deployment and manage resources via REST APIs.
  • Include requirements for setting up private connectivity such as AWS PrivateLink to secure network access paths.

Step 2: Evaluate candidates

Look for portfolios that demonstrate concrete administration of Databricks accounts and workspaces rather than just data analysis. Uma can run instant video interviews and build shortlists with side-by-side comparisons to help you assess technical depth quickly.

  • Verify that candidates have assigned account and workspace admin roles to users, service principals, and groups in previous projects.
  • Check for evidence of managed grants and privileges for securables within a governed Unity Catalog environment.
  • Confirm they have restricted workspace admin permissions through specific admin settings to enforce governance policies.

Step 3: Interview your top choices

Discuss specific scenarios involving identity management and network security architecture during your conversations. Interviews can be scheduled and conducted within Upwork Messages with an immediate transcript and summary after each one.

  • Ask how they use Catalog Explorer or SQL commands to audit and adjust object-level privileges for different teams.
  • Question their approach to configuring cloud-provider specific network security features for restricted access.
  • Explore their method for automating repetitive administrative tasks using the Databricks CLI or API integrations.

Step 4: Agree on scope and begin work

Set clear milestones for delivering configured administration setups and IaC artifacts before work begins. Use Upwork Messages and the contract workroom for communication and project management, plus identity verification, payment protection, hourly tracking, and project funds for security.

  • Define deliverables such as a fully configured workspace with assigned admin roles and access controls.
  • Require Terraform code that provisions and manages Databricks resources according to your infrastructure standards.
  • Establish a final milestone for submitting documentation on networking configurations and enforced permission boundaries.

Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.

The rates and information provided in this article are based on current data and industry sources available at the time of publication. Freelance rates can vary depending on factors such as experience, location, project scope, and market conditions. Readers are encouraged to conduct their own research to confirm current rates and trends, as this information may change over time.

How much does hiring a Databricks Platform specialist cost?

$500-$1,500 per project is a typical range for focused Databricks Platform specialist work. Final pricing depends on scope, technical complexity, required integrations, source-material quality, revision needs, and the freelancer's experience level.

Access control configuration

$500-$1,200/project

Entry-level to mid-level
  • Assigned account and workspace admin roles
  • Configured group-based access controls
  • Recorded user roles and access policies

Unity Catalog governance

$1,200-$2,500/project

Mid-level
  • Granted catalog and schema permissions via SQL
  • Applied object-level security rules
  • Exported current grant states for review

Network security setup

$2,500-$4,500/project

Mid-level to senior-level
  • Established PrivateLink or private endpoints
  • Defined inbound and outbound traffic limits
  • Verified secure access paths and restrictions

Infrastructure automation

$4,500-$7,000/project

Senior-level
  • Authored IaC modules for workspace deployment
  • Automated resource management via REST API
  • Documented execution steps for platform setup

Enterprise governance architecture

$7,000-$12,000/project

Expert-level
  • Enforced workspace admin permission boundaries
  • Integrated Unity Catalog with network security
  • Submitted full governance and security assessment

Frequently asked questions

Is hiring a Databricks Platform specialist worth it?

For most businesses, yes: hiring a Databricks Platform specialist is worthwhile. This role secures your data infrastructure by configuring Unity Catalog permissions and managing workspace access controls. They also automate platform deployment using Terraform to reduce manual configuration errors.

How do I evaluate Databricks Platform specialist candidates?

Evaluate candidates by asking them to describe how they configure Unity Catalog privileges for specific schemas or tables. Look for concrete examples of using SQL commands or the Databricks CLI to manage grants rather than general statements about data governance.

What tools does a Databricks Platform specialist use?

A Databricks Platform specialist uses the Databricks Terraform provider to automate resource management through infrastructure as code. They also employ Unity Catalog SQL commands and the Databricks CLI to administer user access and network security settings.

How does a Databricks Platform specialist secure workspace access?

They secure access by assigning account and workspace admin roles to specific users or service principals. This specialist also configures private connectivity options like AWS PrivateLink to restrict network paths to the platform.