Analytics Engineer II

About the role

You will translate defined business requirements into scalable data models and datasets within Snowflake using SQL, dbt, and Python to support both analytical and operational reporting for the enterprise. You own the development and delivery of data products that are reliable, governed, and aligned with established architecture and engineering standards. In this position, you will contribute directly to data quality, governance, and the enhancement of AI-enabled analytics capabilities across the organization. You will partner closely with Data Engineering, Business Intelligence, business analysts, and other stakeholders throughout the full analytics lifecycle from discovery to deployment. This role provides hands-on involvement in modern data platforms, semantic-layer development, and the implementation and ongoing support of data products. You will apply Snowflake capabilities such as data definition language operations, role-based access control, SQL functions, and Snowflake Cortex to build robust solutions. You will support data quality and governance by applying data validation, security, and quality practices throughout data development and implementation. This opportunity offers collaborative work in an environment that emphasizes progressive professional growth, community engagement, diversity, and work-life balance.

Key facts

What you'll do

Collaborate with business stakeholders to gather and clarify data and reporting requirements and translate them into effective data solutions that address real business needs.

Develop and maintain scalable data models and data products using dbt and Snowflake while adhering to established architecture and engineering standards to ensure consistency and reliability.

Partner with Data Engineering, Business Intelligence, and business teams to design and implement data products across the analytics lifecycle from initial discovery through deployment and optimization.

Contribute to semantic-layer models, business metrics, and standardized definitions that support AI-enabled agents, workflows, and consistent analytics across the enterprise.

Apply Snowflake capabilities including data definition language (DDL) operations, role-based access control (RBAC), SQL functions, and Snowflake Cortex in the development, deployment, and support of data products.

Support data quality and governance by applying data validation, security, and quality practices throughout data development, implementation, and ongoing maintenance.

Participate in code reviews, version control, testing, and continuous integration and continuous delivery (CI/CD) processes in accordance with team standards to ensure high-quality deliverables.

Provide clear project updates, technical documentation, and implementation status to both technical and business stakeholders to maintain transparency and alignment.

Organize and coordinate work effectively across assigned projects while managing priorities and contributing within established engineering and delivery practices.

Translate business requirements into technical specifications and data models that enable accurate, scalable, and performant analytics solutions.

Implement and support data products that enable downstream reporting, analysis, and operational decision-making across the organization.

Monitor data pipelines and datasets to ensure ongoing performance, reliability, and adherence to data standards and governance policies.

Collaborate on the design and evolution of the semantic layer to ensure metrics consistency and clarity for enterprise-wide use.

Leverage SQL, dbt, and Python to build, test, and deploy data transformations and data products that meet defined business objectives.

Requirements

3+ years of professional experience in analytics engineering, data engineering, or business intelligence demonstrating a track record of delivering data solutions.

3+ years of experience with SQL and analytical databases such as Snowflake, Databricks, SQL Server, or Azure to develop and optimize queries and data models.

Experience developing and maintaining data models, pipelines, and reporting datasets that support analytical and operational use cases.

Experience working in Agile environments and collaborating with cross-functional teams to deliver data-driven outcomes.

Ability to communicate effectively with technical and business stakeholders, including providing clear documentation and concise project updates.

Strong analytical, problem-solving, and data validation skills to develop reliable, scalable, and maintainable data solutions.

Capacity to organize and coordinate work effectively across multiple projects while adhering to established engineering and delivery practices.

Understanding of extract, load, transform (ELT) and extract, transform, load (ETL) processes and tools used in modern data platforms.

Exposure to semantic-layer concepts and tools that support consistent metrics, definitions, and AI-enabled analytics.

Comfort working with version control systems, code review practices, and testing approaches in a collaborative development environment.

Willingness to apply Snowflake features such as role-based access control, SQL functions, and Cortex to support data product requirements.

Commitment to data quality, governance, and security practices throughout the data development lifecycle.

Willingness to provide clear project updates, technical documentation, and implementation status to both technical and business stakeholders.

Nice to have

Bachelor's degree in computer science, engineering, data analytics, or a related technical field that provides foundational knowledge for data platform roles.

Experience in wealth management or financial services where analytical solutions support complex decision-making and regulatory considerations.

Experience with dbt for data modeling, testing, documentation, and deployment workflows to ensure reliable and well-documented data transformations.

Experience with Git and version control practices to manage code changes and collaborate effectively within team workflows.

Understanding of extract, load, transform (ELT) and extract, transform, load (ETL) processes and tools to support integration and migration activities.

Exposure to semantic-layer concepts and tools that enable consistent metrics, clear definitions, and AI-enabled analytics across the organization.

Practical notes

This role is full_time.