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Data Architect - AI & Automation

Company Description

LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed.

Join us to transform the way the world works.

Job Description

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.

This role is located in San Francisco or Sunnyvale. 

On the AI & Automation – Central Operations team, our mission is to leverage Data, AI and Advanced Analytics to deliver insights at scale and automate processes. We partner with LinkedIn’s Global Business Organization to find business needs for AI and Automation, scope and design new solutions, develop AI models and data pipelines, manage data products, automate processes, and deliver strategic insights. 

In this role, you will design, build, and maintain large-scale data foundations on Azure that power AI solutions to boost sales productivity. You will expand our Azure data ecosystem to support our analysis of customer interactions and better understand winning sales behaviors. By working with engineering and go-to-market teams, you’ll build data foundations that help improve employee training, deepen customer insights, and drive more successful deals.

Key Responsibilities

  • Build and refine data pipelines using Azure Databricks and Azure Data Factory to move data securely and efficiently across systems with tools like Apache Airflow.

  • Design and enhance scalable Azure architectures to support AI applications powered by large language models.

  • Work with BI developers, data scientists, sales ops, and domain experts to convert business needs into robust technical solutions.

  • Implement best practices for operations, including workflow orchestration, version control, CI/CD, and thorough documentation for quality, performance, and compliance.

  • Act as a technical advisor and subject matter expert, offering guidance on design and process improvements that drive business impact.

Qualifications

Basic Qualifications

  • Bachelor’s degree in Engineering, Computer Science, Data Science, or equivalent experience in a related technical field 

  • 4+  years’ experience in a data engineering,  analytics engineering or a related role 

  • 4+ years experience in Python and SQL.

  • 2+  years’ experience with Databricks and Azure Data Factory.

  • Proven ability to lead cross-functional projects and clearly communicate technical concepts to both technical and non-technical stakeholders.

Preferred Qualifications

  • Master’s degree in Engineering, Computer Science, Data Science, or equivalent advanced experience.

  • 2+ years’ experience implementing operational best practices (e.g., workflow orchestration, Git, CI/CD, Jira).

  • Azure Solutions Architect certification or strong experience designing Azure platforms, including ML/AI tools.

  • Experience optimizing queries on distributed systems, including API integrations.

  • Experience working with unstructured data and large language models.

  • Familiarity with SaaS business models and enterprise sales strategies is a plus.

  • Knowledge of Java is a plus.

Suggested Skills:

  • Databricks
  •  Azure

  • Python

  • Airflow

  •  AI/ML Workflows

LinkedIn is committed to fair and equitable compensation practices.   

The pay range for this role is $106,000-$174,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.   

  The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linkedin.com/benefits. 

Additional Information

Equal Opportunity Statement

LinkedIn is committed to diversity in its workforce and is proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class. LinkedIn is an Affirmative Action and Equal Opportunity Employer as described in our equal opportunity statement here: EEO Statement_2020 - Signed.pdf.

Please reference the following information for more information:  https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf and

 https://www.dol.gov/ofccp/regs/compliance/posters/pdf/OFCCP_EEO_Supplement_Final_JRF_QA_508c.pdf  for more information.

LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful.

If you need a reasonable accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us at [email protected] and describe the specific accommodation requested for a disability-related limitation.

Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to:

  • Documents in alternate formats or read aloud to you
  • Having interviews in an accessible location
  • Being accompanied by a service dog
  • Having a sign language interpreter present for the interview

A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response.

LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information.

San Francisco Fair Chance Ordinance ​

Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records.

Pay Transparency Policy Statement ​

As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.

Global Data Privacy Notice for Job Candidates ​

Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal.

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Average salary estimate

$140000 / YEARLY (est.)
min
max
$106000K
$174000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Data Architect - AI & Automation, LinkedIn

Looking to take your career to the next level? Join LinkedIn as a Data Architect - AI & Automation in the vibrant city of San Francisco! At LinkedIn, we’re all about creating economic opportunities, not just for our members but also for our own team. Picture yourself working in a hybrid setup, enjoying the flexibility to work from home and collaborate in our state-of-the-art offices. In this role, you’ll be instrumental in enhancing our AI & Automation initiatives, leveraging data and advanced analytics to drive insights and automate processes. You’ll work closely with our Global Business Organization to identify needs and create innovative AI solutions that empower our salesforce. Plus, you’ll get your hands dirty building and maintaining data pipelines on Azure, transforming how we analyze customer interactions. If you have a background in data engineering and love collaborating with cross-functional teams, you could be the perfect fit for this exciting opportunity! Your expertise in Azure, along with your ability to convert business needs into powerful technical solutions, will play a crucial role in improving our employee training and sales strategies. Let’s revolutionize the way the world works together at LinkedIn!

Frequently Asked Questions (FAQs) for Data Architect - AI & Automation Role at LinkedIn
What are the primary responsibilities of a Data Architect - AI & Automation at LinkedIn?

As a Data Architect - AI & Automation at LinkedIn, your primary responsibilities will include designing and maintaining large-scale data foundations on Azure, building and refining data pipelines using Azure Databricks and Azure Data Factory, and working closely with various departments to transform business needs into technical solutions. You'll be central in implementing best practices for operations and ensuring that our data architecture supports advanced AI applications.

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What qualifications do I need to apply for the Data Architect - AI & Automation role at LinkedIn?

To apply for the Data Architect - AI & Automation position at LinkedIn, you should have a Bachelor’s degree in Engineering, Computer Science, Data Science, or a related field, along with at least 4 years of experience in data or analytics engineering. Proficiency in Python and SQL is required, and experience with Azure services like Databricks and Data Factory is a plus.

Join Rise to see the full answer
What tools and technologies will I work with as a Data Architect - AI & Automation at LinkedIn?

In the Data Architect - AI & Automation role at LinkedIn, you will work with a variety of modern tools and technologies including Azure Databricks, Azure Data Factory, Apache Airflow for orchestration, and a focus on advanced analytics to support AI models. Familiarity with additional technologies like machine learning tools and workflow orchestration practices will be beneficial.

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How does the Data Architect - AI & Automation role contribute to LinkedIn's overall mission?

The Data Architect - AI & Automation role at LinkedIn plays a vital role in aligning data and technology to support our mission of creating economic opportunities. By leveraging data and AI, you will help drive strategic insights that not only improve sales productivity but also enhance training and support customer engagement, ultimately contributing to LinkedIn's goal of transforming how the world works.

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What is the work culture like for a Data Architect - AI & Automation at LinkedIn?

At LinkedIn, the work culture for a Data Architect - AI & Automation is focused on trust, inclusion, and collaboration. You can expect a hybrid working environment that encourages flexibility while fostering close connections with colleagues. The company also prioritizes personal growth and investment in employee development, aiming to create a fun and supportive atmosphere where everyone can thrive.

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Common Interview Questions for Data Architect - AI & Automation
Can you describe your experience with Azure Databricks as a Data Architect?

When answering this question, highlight your specific projects involving Azure Databricks, explaining how you utilized it to build data pipelines or process data. Talk about any challenges you faced and how you overcame them, and mention best practices you've implemented to optimize performance.

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What strategies do you use for ensuring data quality in your pipelines?

Discuss specific strategies such as implementing data validation checks, using unit tests for data processes, and regular monitoring of data flows. Mention any tools or frameworks that support these quality control processes, emphasizing your focus on accuracy and reliability.

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How do you approach collaborating with cross-functional teams?

In your response, emphasize your communication skills and how you foster relationships with different stakeholders. Share examples of past collaborations, focusing on how you converted business requirements into actionable data strategies while ensuring everyone is aligned with the project's objectives.

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What are some challenges you've encountered while working with unstructured data?

Identify specific challenges like data inconsistency, integration issues, or processing difficulties with unstructured data. Discuss how you approached these hurdles, what tools or methodologies you used, and any successful outcomes that resulted from your efforts.

Join Rise to see the full answer
How do you implement CI/CD practices in your data projects?

Share your approach to continuous integration and continuous deployment within data projects. Mention the tools you use to facilitate this, such as Git for version control and any CI/CD pipelines you've established, along with their impact on your team's productivity and project success.

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Can you explain your process for designing scalable data architectures?

Discuss the key principles you adhere to when designing scaling architectures. Highlight your emphasis on modularity, efficient data flow, and strategic use of cloud resources. Provide an example of a successful architecture you've built and its scalability features.

Join Rise to see the full answer
What techniques do you use to optimize SQL queries?

Provide specific techniques such as indexing, query rewriting, or using optimizer hints. Support your answer with examples of queries you've optimized and the performance improvements achieved, demonstrating your understanding of balancing efficiency and resource consumption.

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How do you stay updated with new developments in AI and data engineering?

Mention resources like online courses, webinars, or industry conferences you attend. Discuss any communities or professional networks you're part of, emphasizing your proactive approach to learning and continuous improvement in your field.

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Could you describe a project where you successfully implemented a machine learning model?

Share a detailed account of a specific project, breaking down the problem, your approach, and the model's performance. Highlight any collaboration with data scientists and how your architectural decisions influenced the model’s success.

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What do you consider when advocating for best practices in data operations?

Discuss the importance of performance, compliance, and thorough documentation in data operations. Share how you communicate these best practices to the team and provide examples of initiatives you've led to enhance operational efficiency.

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Our mission is to create economic opportunity for every member of the global workforce and this vision connects our more than 16,000 employees in dozens of offices across five continents. It inspires us to invest in our talent, support career grow...

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DATE POSTED
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