Staff Analytics Engineer

Huntress

20h•$170,000 - $200,000•Remote

About The Position

Analytics engineer is a hybrid Data Engineer/Data Scientist/Business Analyst role that has the ability to understand data flows end to end, and the engineering toolkit to extract the most value and solve business problems. As a Staff Analytics Engineer at Huntress, you will be a key technical leader, driving the strategy, architecture, and execution of our data analytics platform. You'll provide thought leadership, architect cutting-edge solutions, and serve as a technical mentor to the rest of the team by providing expertise and code reviews to unlock the full potential of our data. Your work will directly influence business strategy and drive growth in a dynamic startup environment. This is a great role for a seasoned expert passionate about building scalable, high-impact data systems.

Requirements

7+ years of progressive experience in analytics engineering, data engineering, or a similar role, with a strong emphasis on architecting and implementing large-scale data solutions.SaaS experience is a plus.
Financial & Go-to-Market Data Experience: Familiarity with data producers supporting Financial, Marketing, and Sales data initiatives and the handling of sensitive PII and board level reporting across a broad stakeholder base.
Data Modeling Expertise: Mastery of developing modular and reusable data models to accelerate self-service analytics (e.g. star schemas, snowflake schemas). Experience migrating legacy architectures & data models is a plus.
Expert-level proficiency with cloud data warehousing technologies such as Snowflake (preferred), Redshift, or BigQuery.
Extensive experience developing and optimizing complex ETL/ELT programs and data pipelines using tools like DBT, Fivetran, Airflow, etc. Expertise in query performance tuning, materialization strategies, and data transformation.
Data Visualization: Proficient in building polished dashboards in tools like Looker, Sigma, Tableau.
Proficiency with AI Tools: Expertise in prompt engineering and design for LLMs (e.g., GPT) including creating, refining, and optimizing prompts to internal use cases and the end to end process of delivering data products.
Demonstrated ownership of full life cycle data analytics development: Strategic Planning, Requirements, Architecture, Design, Testing, Deployment, and Operations.
Exceptional presentation, communication, and interpersonal skills, with the ability to articulate complex technical ideas to both technical and non-technical audiences, including C-level executives, and drive consensus.
Intermediate to Advanced Python: proficient in data science languages (e.g Python, R) for advanced data manipulation, statistical modeling and ML
Intermediate to Advanced experience with a wide range of Machine Learning and analytical techniques, their real-world advantages/drawbacks, and experience deploying models to production.
Strong strategic thinking, problem-solving, and decision-making capabilities.
A bachelor’s or master’s degree in Computer Science, Technology, Engineering, or a related field; or equivalent deep industry experience.

Responsibilities

Architect, design, and lead the implementation of highly complex, scalable, and resilient data solutions in the cloud, leveraging AWS, Snowflake, dbt, Fivetran, and other modern technologies.
Be the Expert. Quickly build subject matter expertise in a specific business area and data domain. Understand the data flows from creation, ingestion, transformation, and delivery.
Examples: Embed into a new line of business and work with engineering and finance partners to deliver initial data models and insights. Communicate with the engineering teams to fix data gaps (e.g. missing data objects or attributes) and take accountability for fixing issues anywhere in the stack.
Support defining and executing the overarching strategy for the analytics engineering function, including the development and evangelization of data frameworks, standards, and best practices across the organization.
Lead efforts in designing, building, and maintaining a robust, governed, and scalable semantic layer to provide consistent and reliable data access for business intelligence and analytics.
Spearhead the technical vision and roadmap for data quality and governance, establishing frameworks and processes to ensure data integrity and proactively address systemic issues.
Act as a primary technical consultant to senior executives and business stakeholders, translating complex data concepts into actionable insights and strategic recommendations.
Mentor, coach, and develop junior and mid-level analytics engineers, fostering a culture of technical excellence, innovation, and continuous learning within the team.
Set standards for documentation, conduct advanced peer code reviews, and define comprehensive testing strategies for data solutions.
Continuously evaluate and champion new technologies and methodologies to enhance the data and analytics capabilities at Huntress.