Analytics engineers are essential to modern data teams. As companies move from scattered, siloed datasets toward more structured, reusable source-of-truth systems, these experts are brought on to turn complex data into clean, tested models that other in-house counterpart teams can actually understand and even use to their advantage. Now that there’s more data generation than ever, demand for these roles has grown. By 2030, the market will see an estimated 300,000 data-related jobs added, according to the U.S. Bureau of Labor Statistics.
This list includes some of the most prolific employers contributing to the data-gig boom, frequently hiring analytics engineers who know how to transform and model raw data into production-ready datasets at scale.
Top Companies Hiring Analytics Engineers
- Databricks
- Snowflake
- dbt Labs
- Netflix
- Airbnb
Top Companies Hiring Analytics Engineers
Headquarters: San Francisco, California
Founded: 2013
Company size: 10k - 50k employees
DoorDash, the market share winner of U.S.-based food delivery apps, relies on analytics engineers to solve three-sided marketplace problems involving consumers, dashers and merchants. With more than 50 million active users, the company hires specialists who can build scalable data models within its Snowflake and dbt Labs stack in roles that focus on improving logistics and delivery efficiency, dispatch systems, delivery times and how orders move through the platform.
Headquarters: San Francisco, California
Founded: 2008
Company size: 5k - 10k employees
Rentals marketplace Airbnb is widely recognized for its in-house Minerva metrics platform, a centralized system that ensures data consistency across the entire organization. The company is currently hiring for software engineer managers who can help define and maintain these standardized metrics, build reliable data models and support experimentation frameworks across its millions of listings.
Headquarters: Los Gatos, California
Founded: 1997
Company size: 14k+ employees
The world’s No. 1 streaming platform, Netflix, maintains a world-class data culture where analytics engineers are on the hook to deliver quality, uninterrupted content for more than 300 million subscribers. The organization takes in candidates who can write efficient ETL code that balances extreme data volume with the low-latency requirements of a global product team.
Headquarters: San Francisco, California
Founded: 2013
Company size: 7k+ employees
As the pioneer of the “data lake” architecture, Databricks provides the infrastructure for unified data and artificial intelligence to more than 15,000 global organizations. The company hires analytics engineers to build and maintain the internal Gold-layer tables that power its corporate health metrics, in roles that require advanced proficiency in Spark, SQL and dbt.
Headquarters: San Jose, California
Founded: 1984
Company size: 86,000+ employees
As the world’s largest networking hardware and software company, Cisco plays a central role in facilitating the global internet. Through its routers, switches and security systems, the company underpins the enterprise networks and data centers that move and manage the lion’s share of digital traffic every day. It recruits analytics engineers to its infrastructure and supply chain teams, where the work centers on building scalable data pipelines and analytics platforms using tools like Apache Spark and Kubernetes to boost network performance and detect cybersecurity threats.
Headquarters: San Francisco, California; Dublin, Ireland
Founded: 2010
Company size: 5k - 10k employees
Stripe processes about 50,000 transactions every minute. In 2025 alone, the fintech company was responsible for moving upwards of $1.9 trillion in total via its platform. Shopify hires analytics engineers to build financial-grade data models, seeking out people who can keep numbers consistent across systems while making complex tax and regulatory reporting easier for global clients.
Headquarters: Menlo Park, California
Founded: 2012
Company size: 6.7k+ employees
Data platform Snowflake stands out for its cloud-native architecture, which separates storage and compute so companies can scale data usage independently without reworking their infrastructure. That flexibility, along with its ability to handle structured and semi-structured data efficiently across multiple cloud environments, is why more than 12,000 clients rely on it for centralized analytics, data sharing and high-performance querying. The company frequently hires analytics engineers in roles like solutions architects and engineers, as well as AI and machine learning specialists, to strengthen its own data cloud.
Headquarters: Philadelphia, Pennsylvania
Founded: 2016
Company size: 500 - 1k employees
dbt Labs essentially defined the modern analytics engineering discipline by introducing a framework that applies software engineering best practices — like version control, testing and modular design — to SQL workflows. The organization looks for analytics engineers who can translate raw data pipelines into the insights that teams can actually use. Generally speaking, ideal candidates can write clean, reusable code with Jinja macros and maintain solid CI/CD workflows.
Headquarters: Ottawa, Canada
Founded: 2006
Company size: 5k - 10k employees
Shopify’s e-commerce platform powers millions of businesses globally, facilitating more than $1.1 trillion in total sales since its launch. Managing a massive volume of transactional data, the company prioritizes analytics engineers who are comfortable working remotely and can build clean, reliable data models that ensure accurate insights from merchant to merchant as the platform continues to scale.
Headquarters: San Francisco, California
Founded: 2009
Company size: 30k+ employees
Uber’s high-frequency rideshare and food delivery platform requires live data modeling as it carries out 40 million trips per day. To support features like dynamic pricing and accurate ETAs, the company recruits analytics engineers to build the foundational data layers for its Michelangelo AI platform who know how to model data for both historical reporting and real-time machine learning features.
