Analytics Engineer Jobs
Analytics Engineer jobs are open across fintech, e-commerce, healthcare, and tech platforms, at every level from new-grad to staff and principal, with specializations in data modeling, dbt workflows, and data warehouse architecture. Find a role that fits from the openings below and apply directly.
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Data Engineer, Product Analytics Responsibilities:
- Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems
- Create and contribute to frameworks that improve the efficacy of logging data, while working with data infrastructure to triage issues and resolve
- Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights visually in a meaningful way
- Define and manage Service Level Agreements for all data sets in allocated areas of ownership
- Determine and implement the security model based on privacy requirements, confirm safeguards are followed, address data quality issues, and evolve governance processes within allocated areas of ownership
- Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains
- Solve our most challenging data integration problems, utilizing optimal Extract, Transform, Load (ETL) patterns, frameworks, query techniques, sourcing from structured and unstructured data sources
- Assist in owning existing processes running in production, optimizing complex code through advanced algorithmic concepts
- Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts
- Influence product and cross-functional teams to identify data opportunities to drive impact
- Mentor team members by giving/receiving actionable feedback
Minimum Qualifications:
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- 7+ years of experience where the primary responsibility involves working with data. This could include roles such as data analyst, data scientist, data engineer, or similar positions
- 7+ years of experience with SQL, ETL, data modeling, and at least one programming language (e.g., Python, C++, C#, Scala or others.)
Preferred Qualifications:
- Master's or Ph.D degree in a STEM field
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
$177,000/year to $247,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.
Analytics Engineer Jobs by Experience Level
Top Cities Hiring Analytics Engineers
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Find Analytics Engineer JobsAnalytics Engineer Job Market
Who's Hiring
- Amazon25

- Amazon Web Services18

- Apple10

- Meta8

- DoorDash6

Top Industries Hiring
- Technology & Software64
- Retail11
- E-Commerce & Online Marketplaces11
- Consulting & Professional Services11
- Distribution & Wholesale10
What Employers Look For
The qualifications that appear most often in analytics engineer jobs.
- Proficiency in SQL and experience writing complex analytical queries
- Hands-on experience with dbt for data modeling and transformation
- Experience with a cloud data warehouse such as Snowflake, BigQuery, or Redshift
- Ability to work with data pipelines and orchestration tools like Airflow or Dagster
- Bachelor's degree in computer science, statistics, mathematics, or a related field
- Experience collaborating with data analysts and data engineers on cross-functional projects
Tips for Your Analytics Engineer Job Search
Show dbt and SQL project work
Hiring managers expect to see your data models, not just your job titles. Link to a public GitHub repo with dbt projects, documented schemas, or transformation logic you've built. Portfolios that show modeling decisions beat resumes that only list tools.
Tailor your resume to the data stack
Analytics engineer postings vary sharply by stack. Read each job description for the specific warehouse (Snowflake, BigQuery, Redshift) and orchestration tool (Airflow, Dagster) and mirror that language exactly in your resume's skills and project bullets.
Filter openings by data maturity stage
Early-stage companies need you to build pipelines from scratch. Mature data orgs need you to refactor legacy models. Knowing which environment you want before you apply saves time and helps you ask sharper questions during screening calls.
Apply early to roles that fit
Migrate Mate lists analytics engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare for a take-home modeling exercise
Many analytics engineer interviews include a take-home where you model a raw dataset in dbt. Practice writing staging, intermediate, and mart layer models on public datasets so you can deliver clean, documented work under a time constraint.
Negotiate using scope, not just title
Analytics engineer leveling varies widely across companies. When negotiating, ask about data team size, model ownership, and roadmap influence. These factors define your actual role more than the title does, and clarifying them gives you real leverage.
Analytics Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most analytics engineers?
The companies hiring the most analytics engineers right now include Amazon, Amazon Web Services, and Apple, with the largest share of openings in California, New York, and Washington, based on current listings on Migrate Mate as of August 2026. Demand is concentrated at companies with large self-serve data programs and modern cloud warehouses.
How many analytics engineer jobs are remote?
About 63% of analytics engineer openings are fully remote or hybrid as of August 2026, making it one of the more remote-accessible technical roles. Data modeling and dbt-heavy work tends to be the most remote-friendly sub-area, while roles that require close collaboration with embedded business teams more often require on-site presence.
How do you become an analytics engineer?
Start by building strong SQL skills and learning dbt on a public dataset you can share. Get comfortable with one cloud data warehouse, then practice modeling raw data into clean, documented layers. Build a portfolio project that shows staging, intermediate, and mart models. Apply to junior or data analyst roles at companies with modern data stacks to get your first hands-on production experience.
How do you get hired as an analytics engineer with little experience?
Focus on demonstrating modeling judgment rather than years on the job. Build a dbt project on an open dataset, document your decisions in a README, and publish it publicly. Highlight any SQL work from analyst or engineering roles. Many teams hire candidates with strong SQL and analytical foundations and train them on the specific stack, so framing your background around data thinking helps.
What does the analytics engineer interview process look like?
The process typically starts with a recruiter screen focused on your background and the data stack you've used. A technical phone screen follows, often involving live SQL or a short modeling question. Most companies then assign a take-home exercise where you model a raw dataset in dbt. Final rounds include a technical deep-dive with the data team and a cross-functional conversation with stakeholders or analysts you'd support.
Where can I find and apply to analytics engineer jobs?
You can find and apply to analytics engineer jobs on Migrate Mate, which lists current openings from across the United States. Find roles that fit your experience, stack, and preferred work arrangement, then apply directly to each listing.
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