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Analytics Engineer Jobs (NOW HIRING)

Analytics Engineer

Manhattan, NY ยท On-site +1

$121K - $175K/yr

Analytics Engineers play a critical role in discovering, extracting, storing, cataloging, modeling, and processing our data. As a member of this team you will own our analytics data layer. You'll ...

Analytics Engineer

New York, NY ยท On-site

$100K - $140K/yr

Role Overview We're looking for an Analytics Engineer to join as an early member of our Data Team and help build the systems that power artist sourcing, track pricing, portfolio management, and ...

Analytics Engineer

Manhattan, NY ยท On-site

$100K - $140K/yr

Role Overview We're looking for an Analytics Engineer to join as an early member of our Data Team and help build the systems that power artist sourcing, track pricing, portfolio management, and ...

Role Overview We're looking for an Analytics Engineer to join as an early member of our Data Team and help build the systems that power artist sourcing, track pricing, portfolio management, and ...

Analytics Engineer

New York, NY ยท On-site +1

$121K - $175K/yr

Analytics Engineers play a critical role in discovering, extracting, storing, cataloging, modeling, and processing our data. As a member of this team you will own our analytics data layer. You'll ...

Analytics Engineer

New York, NY ยท On-site

$100K - $140K/yr

Role Overview We're looking for an Analytics Engineer to join as an early member of our Data Team and help build the systems that power artist sourcing, track pricing, portfolio management, and ...

About The Role Fora is hiring its first Senior Analytics Engineer to help scale how data is modeled, curated, and delivered across the company. As our centralized data team grows, we're looking for a ...

Analytics Engineer

New York, NY ยท On-site

$121K - $175K/yr

Analytics Engineers play a critical role in discovering, extracting, storing, cataloging, modeling, and processing our data. As a member of this team you will own our analytics data layer. You'll ...

In the role of Senior Analytics Engineer, you will lead the design of complex analytics domains, establish modeling standards, and partner with stakeholders to ensure analytics deliver measurable ...

In the role of Senior Analytics Engineer, you will lead the design of complex analytics domains, establish modeling standards, and partner with business stakeholders to ensure analytics deliver ...

About the Team The Analytics Engineering team at DoorDash is embedded within the Analytics and Data Engineering Orgs, and is responsible for building internal data products that scale decision-making ...

Role Overview The Senior Analytics Engineer I will help establish best analytical practices across the GOAT Group organization. Ideal candidates will have a deep understanding of data modeling and ...

Role Description The Analytics Engineer will play a key role in developing and maintaining internal reporting solutions that deliver insights into portfolio performance, product development, and ...

Analytics Engineer Location : Philadelphia, PA Duration : long Job Summary A highly experienced analytics engineer with experience of working with large-scale, distributed data pipelines, you will be ...

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Showing results 1-20

Analytics Engineer information

See salary details

$62.5K

$109.1K

$178K

How much do analytics engineer jobs pay per year?

As of Jun 4, 2026, the average yearly pay for analytics engineer in the United States is $109,135.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,500.00 and $122,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Analytics Engineer, and why are they important?

To thrive as an Analytics Engineer, you need a strong foundation in data modeling, SQL, and analytics engineering principles, often supported by a degree in computer science, data science, or a related field. Proficiency with data transformation tools such as dbt, cloud data warehouses like Snowflake or BigQuery, and version control systems like Git is essential. Strong problem-solving skills, communication, and collaboration abilities help translate business needs into scalable data solutions and foster teamwork. These skills and qualities are crucial for ensuring data quality, building reliable analytics infrastructure, and enabling data-driven decision-making across organizations.

How does an Analytics Engineer typically collaborate with data scientists and business stakeholders on projects?

Analytics Engineers play a critical bridge role between data engineering and data analysis. They work closely with data scientists to transform raw data into clean, reliable datasets that are ready for advanced analytics or modeling. At the same time, they collaborate with business stakeholders to understand reporting needs, ensuring that data models align with business goals. Regular communication and iterative feedback are key, as Analytics Engineers often gather requirements, build data pipelines, and adjust data products based on stakeholder input.

What is an Analytics Engineer?

An Analytics Engineer is a professional who bridges the gap between data engineering and data analysis. They are responsible for designing, building, and maintaining data models, pipelines, and analytics tools that enable organizations to make data-driven decisions. Analytics Engineers often work closely with data analysts and business stakeholders to ensure clean, reliable, and well-structured data is available for reporting and analysis. Their work typically involves using SQL, data transformation tools like dbt, and cloud data warehouses to create scalable and efficient data solutions.

What is the difference between Analytics Engineer vs Data Engineer?

AspectAnalytics EngineerData Engineer
CredentialsOften requires SQL, Python, data modeling certificationsRequires similar skills, often with additional focus on infrastructure and systems
Work EnvironmentFocuses on data analysis, visualization, and reportingBuilds data pipelines, manages data infrastructure
Industry UsageCommon in analytics teams, BI, and data-driven rolesPrevalent in data engineering, data platform teams

While both roles work closely with data, Analytics Engineers primarily focus on transforming data for analysis and visualization, whereas Data Engineers build the infrastructure and pipelines that enable data access. Understanding these differences helps in choosing the right career path or job role.

More about Analytics Engineer jobs
What cities are hiring for Analytics Engineer jobs? Cities with the most Analytics Engineer job openings:
What are the most commonly searched types of Analytics Engineer jobs? The most popular types of Analytics Engineer jobs are:
What states have the most Analytics Engineer jobs? States with the most job openings for Analytics Engineer jobs include:
Infographic showing various Analytics Engineer job openings in the United States as of May 2026, with employment types broken down into 95% Full Time, 2% Part Time, and 3% Contract. Highlights an 79% Physical, 6% Hybrid, and 15% Remote job distribution, with an average salary of $109,135 per year, or $52.5 per hour.
Sr. Analytics Engineer

Sr. Analytics Engineer

Natures Sunshine Products

Lehi, UT โ€ข On-site

Full-time

Posted 7 days ago


Job description

About the Role

Nature's Sunshine is building a modern enterprise data platform on Microsoft Fabric. This is an opportunity to join early in that journey and help define the data models, semantic layers, and metric standards that shape how the business makes decisions.

As a Senior Analytics Engineer, you will lead the design of trusted analytics assets for a primary business domain, including scalable data models, canonical datasets, governed metrics, and semantic layers. Your work will help define how key business logic is calculated, reused, trusted, and understood across the organization.

This is not a traditional reporting role, and it is not primarily a pipeline engineering role. You will own the layer where business logic becomes trusted, reusable data products: dimensional models, semantic layers, governed metrics, and analytics assets that teams across the business rely on.

You will work through ambiguity, align stakeholders on shared definitions, and build data products that other teams can confidently use and build upon. Success in this role will be measured by adoption, consistency, trust, and maintainability - not output volume.

You will also help shape how we use AI in analytics engineering: accelerating development, improving documentation, generating tests, strengthening data quality, and reducing repetitive work while applying the judgment needed to protect trust in the data.

What You'll Do

  • Build scalable dimensional models, canonical datasets, and semantic layers for reporting, analytics, and decision-making.
  • Define and govern KPIs, metrics, calculations, and reusable business logic with stakeholders.
  • Translate complex or conflicting business logic into clear, maintainable, well-documented data structures.
  • Own modeling decisions across grain, relationships, performance, usability, cost, governance, and maintainability.
  • Partner with business leaders, analysts, and data team members to turn ambiguous questions into durable analytics solutions.
  • Establish practical analytics development standards, including naming conventions, documentation, certification, reusable measures, and change control.
  • Improve trust in reporting by consolidating duplicated logic, clarifying ownership, and making shared metrics easier to understand and maintain.
  • Experiment with AI-assisted workflows to improve development speed, documentation, testing, data quality, and model design.

What We're Looking For

Required Qualifications

  • 5+ years of experience in analytics engineering, BI engineering, data modeling, or a closely related analytics role.
  • Advanced SQL skills, including experience building complex, performant transformations.
  • Strong understanding of dimensional modeling, including facts, dimensions, grain, relationships, and conformed dimensions.
  • Experience designing production-ready data models, semantic layers, BI models, or trusted datasets used for reporting and analysis.
  • Proven ability to work through ambiguous business requirements, reconcile conflicting definitions, and translate decisions into scalable data models.
  • Ability to communicate clearly with both technical and non-technical stakeholders.
  • Strong judgment in balancing speed, quality, performance, usability, cost, and governance when designing analytics assets for long-term use.
  • Experience working in a modern analytics platform such as Microsoft Fabric, Snowflake, Databricks, BigQuery, or similar.

Preferred Qualifications

  • Hands-on experience with Microsoft Fabric.
  • Advanced Power BI experience, including DAX, relationships, calculation groups, performance optimization, or semantic model governance.
  • Experience with Python, data quality testing, version control, CI/CD, or other practices that improve analytics engineering reliability and maintainability.
  • Experience treating analytics assets as products, including certification, adoption, documentation, change management, and lifecycle ownership.
  • Experience leading metric-definition alignment across stakeholders or improving trust in enterprise reporting.

Who Will Thrive in This Role

You will thrive here if you want more than a dashboard-building role. This is a role for someone who enjoys turning messy business logic into trusted data products, influencing how metrics are defined, and building systems that other teams rely on.

You should be comfortable working through ambiguity, challenging unclear requirements, and balancing practical delivery with long-term maintainability.

You should also be excited to help define what modern analytics engineering looks like in an AI-enabled environment: using AI to move faster and improve quality while bringing the technical judgment, business context, and accountability needed to maintain trust in the data.

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Nature's Sunshine is dedicated to being a Force of Nature that champions social and environmental wellness. We are focused on building a team of professionals with diverse backgrounds and experiences to become the natural supplement company of the future. By celebrating the individuality and unique perspectives of our workforce, we empower our employees to share the healing power of nature with more people around the world. And through our commitment to sustainable processes, renewable energy usage and waste reduction initiatives, we're devoted to preserving nature and its power for future generations.

We believe we are stronger together, and our ongoing commitment to diversity, equity, inclusion and belonging ensures that every employee is treated with fairness and respect. Because doing what's right-in the right way-is how we succeed as a company and a society.