Senior Analytics Engineer

Nightingale College; Salt Lake City (UT); On-site; USD 124,000 - 135,000; Full time

Job summary

An established industry player is seeking a Senior Analytics Engineer to lead the design and development of scalable data models and pipelines. This pivotal role will involve collaborating with cross-functional teams to tackle complex data challenges, ensuring data quality, and empowering stakeholders with actionable insights. As a bridge between technical and non-technical teams, you'll foster a culture of data-driven decision-making while utilizing advanced technologies to optimize data processes. If you are passionate about data and eager to drive innovation, this is the perfect opportunity for you to make a significant impact in a dynamic environment.

Qualifications

• 5-9+ years in data engineering or analytics engineering with strong data modeling skills.
• Expertise in SQL, Python, Spark, and modern BI tools like Tableau and Power BI.

Responsibilities

• Design and develop scalable data models and pipelines for actionable insights.
• Collaborate with cross-functional teams to meet evolving data needs.

Skills

Data Modeling

Data Governance

SQL

Python

Spark

Predictive Modeling

Statistical Analysis

Education

Bachelor's degree in Statistics, Computer Science, or Mathematics

Advanced degrees preferred

Tools

Sigma

Tableau

Power BI

dbt

Airflow

Airbyte

Fivetran

Snowflake

Redshift

BigQuery

Job description

Senior Analytics Engineer

The target hiring range for this position is $124,000-135,000.

Position Summary:

As a Senior Analytics Engineer, you will play a critical role in designing and developing scalable data models and pipelines that empower our business with actionable insights. Embedded within the Analytics and Data Services organization, you will collaborate with cross-functional teams and stakeholders across the organization—to solve complex data challenges and drive innovation in our data platform.

You will act as a bridge between technical and non-technical teams, ensuring that data models are robust, data quality is high, and insights are accessible. This role is integral to fostering a culture of data-driven decision-making across the organization.

Key Responsibilities:

• Data Modeling & Pipelines: Architect and develop data models and curated datasets that form the foundation of analytics and reporting. Partner with Data Engineers to build efficient, scalable, and cost-effective data pipelines.
• Collaboration & Partnership: Work closely with business analysts, data scientists, and cross-functional teams to understand data requirements and ensure data models and pipelines meet the business's evolving needs.
• Analytics Enablement: Educate and empower stakeholders to utilize data assets and BI tools effectively (e.g., Sigma, Tableau, Power BI). Support self-service analytics and create data products that meet business objectives.
• Data Governance & Quality: Implement and enforce data integrity standards and best practices to ensure data is consistent, accurate, and reliable. Develop observability automation to monitor the quality of both batch and real-time data streams.
• End-to-End Solution Design: Lead the design and execution of scalable solutions that transform raw data into actionable insights, focusing on performance, cost-effectiveness, and adaptability.
• Innovation & Automation: Utilize advanced technologies (e.g., dbt, Airflow, Airbyte, Fivetran) to optimize ETL/ELT pipelines. Collaborate with platform engineering teams to co-design new workflows and capabilities.
• Predictive & Advanced Analytics: Leverage statistical methods, machine learning models, and advanced analytics to build predictive features and metrics that align with business goals.

Qualifications and Education Requirements:

• Education: Bachelor's degree in a related field (e.g., Statistics, Computer Science, Mathematics) or equivalent industry experience. Advanced degrees are preferred but not required. Advanced certifications in data analysis or data governance (e.g., CDMP, CBIP) are a plus.
• Experience: 5-9+ years of experience in data engineering, analytics engineering, or BI engineering, with proven experience in building and optimizing data models and pipelines. Strong understanding of data governance, data quality, and analytics best practices. Experience with modern BI tools like Sigma, Power BI, or Tableau. Expertise in predictive modeling, statistical analysis, and developing business-aligned metrics.
• Technical Skills: Expertise in SQL, Python, and Spark for ETL/ELT processes. Proven experience in designing and deploying complex data architectures in cloud environments (AWS, GCP, or Azure), modern data architectures (data lakes, warehouses, mesh), and tools such as dbt, Airflow, and Databricks. Experience with large-scale data warehouses (e.g., Snowflake, Redshift, BigQuery) and streaming data systems.