Senior Analytics Engineer, Go-To-Market Data

This role will be based in Sunnyvale San Francisco Chicago or New York. 

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.   

The Analytics Engineer Marketing Strategy & Technology Data Foundations (Staff) will lead and scale foundational data initiatives that enable the Marketing Strategy & Technology organization to make better decisions improve operations and drive business impact. As a senior member of the team you will partner closely with Sales Strategy & Operations Engineering and Data teams to understand business pain points and translate them into scalable data solutions and long-term architecture. 

This is not a purely pipeline development role. We are looking for an Analytics Engineers who understand modern data platforms and data pipelines but can operate at a higher level designing durable data foundations shaping data architecture and creating solutions that make workflows simpler and more effective for business users. 

This is a highly visible role for someone who thrives at the intersection of data architecture analytics engineering and business strategy. You will influence how data is structured governed and consumed across the organization while balancing technical excellence with strong stakeholder partnership. 

The ideal candidate combines strong technical expertise (SQL Spark Trino Python data modeling) with experience leading complex data initiatives influencing cross-functional teams and working closely with business partners to turn ambiguous problems into scalable solutions. 

Responsibilities 

• Lead high-impact data initiatives from strategy through execution including solution design prioritization delivery adoption and long-term ownership across cross-functional teams. 
• Partner closely with Sales and business stakeholders to understand pain points and design scalable data foundations architectures and workflows that improve decision-making and operational efficiency. 
• Architect and evolve reliable business-critical datasets and data products with strong standards for data quality governance monitoring and SLA performance. 
• Identify opportunities to simplify the data ecosystem reduce technical debt and improve trust in data; translate business needs into durable and scalable solutions. 
• Establish analytics engineering best practices including data modeling ownership documentation monitoring and scalable operating standards. 
• Define and measure success through reliability adoption business impact and operational outcomes. 
• Drive adoption of trusted data products and enable self-service insights across teams. 
• Influence and align business engineering and data stakeholders ensuring clear ownership decision-making and execution. 
• Mentor team members and promote scalable system design strong technical practices and operational excellence. 

Qualifications :

Basic Qualifications 

• Bachelors degree in Computer Science Data Science Information Systems Statistics Applied Mathematics Engineering Business Analytics or equivalent practical experience. 
• 6 years of experience delivering data and analytics solutions in a business environment (e.g. analytics engineering data architecture business intelligence data infrastructure data management or consulting). 
• 4 years of experience using SQL and distributed data technologies (e.g. Trino Presto Spark SQL) to design build and optimize large-scale datasets and data workflows. 
• 4 years of experience designing and operating scalable reliable data foundations including data modeling monitoring data quality governance or operational ownership. 
• Experience partnering with business stakeholders to translate ambiguous requirements into scalable technical solutions and measurable business outcomes. 
• 1 years of experience working with GenAI technologies and frameworks (e.g. LLM APIs agent frameworks AI-enabled analytics workflows). 

Preferred Qualifications 

• Passion for applying AI and data capabilities to improve business decision-making and operational efficiency. 
• Experience leading large-scale data initiatives including architecture design migrations operating model improvements or cross-functional transformation efforts. 
• Strong experience with modern data platforms and distributed systems (e.g. Spark Hadoop Airflow InDBT cloud data ecosystems). 
• Experience designing business-critical datasets data products or self-service analytics solutions with clear ownership and adoption strategies. 
• Familiarity with BI and visualization tools (e.g. Tableau Power BI) and best practices in data modeling governance and documentation. 
• Demonstrated ability to influence and align business engineering and data stakeholders including presenting technical concepts to senior leadership. 
• Experience working with CRM and go-to-market data ecosystems (e.g. Salesforce Microsoft Dynamics sales marketing or advertising data domains). 
• Track record of improving data usability trust reliability and operational efficiency through scalable system and process design. 
• Comfortable operating in ambiguous fast-paced environments with a strong bias toward action. 

Suggested Skills: 

• Modern Data Platforms 
• Data Architecture 
• Data Governance 
• Data Quality 
• Business Partnership 
• Cross-Functional Leadership 

LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $138000 - $225000.  

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  Information :

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Employment Type :

Full-time