Senior Analytics Engineer, Go-To-Market Data

LinkedIn•Mountain View, CA

4d•$138,000 - $225,000•Hybrid

About The Position

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.

Requirements

Bachelor’s 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).

Nice To Haves

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.

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.