Senior Analytics Engineer

829 Studios

15d•$75,000 - $110,000•Remote

About The Position

829 is hiring a Senior Analytics Engineer to join our Analytics & AI practice and deliver high-impact BI and data engineering work for our clients. This is a dedicated practice role focused entirely on client engagements, working directly with clients to scope projects, design data architecture, and build solutions that turn raw data into business outcomes. This is a hands-on, individual contributor role with significant client-facing responsibility. You will run discovery, define requirements, design data models, build ETL pipelines, develop dashboards, and present insights to client stakeholders ranging from analysts to C-suite executives. You will own the end-to-end delivery of analytics engagements and serve as the trusted technical advisor to the clients you work with. The right candidate is equally comfortable writing SQL and dbt models, leading a workshop with a client's marketing leadership team, and working through ambiguous data problems independently. You bring strong consulting instincts, deep technical expertise, and the discipline to ship great work in a fast-moving environment. This role sits within 829's Analytics & AI practice and reports to the SVP of Analytics & AI.

Requirements

Bachelor's degree in Computer Science, Data Science, Engineering, or a related quantitative field.
5+ years of experience in BI solution development and implementation, ideally in a consulting or agency environment.
Demonstrated experience leading client-facing engagements and presenting to executive stakeholders.
3+ years of hands-on Python experience, including data manipulation, automation, and pipeline development.
4+ years of strong SQL experience, preferably with Snowflake or BigQuery.
4+ years of experience building dashboards and reports in modern BI tools (Tableau, Sigma, or similar).
Working experience with dbt for data transformation and modeling.
Proven experience designing ETL processes, data models, and BI solution architectures from the ground up.
Knowledge of AI/ML operations and experience maintaining models in production.
Strong problem-solving, communication, and project management skills.
Ability to work independently and deliver results in ambiguous situations.

Nice To Haves

Master's degree in a relevant field.
Experience with cloud platforms (AWS, Azure, or GCP) and data governance frameworks.
Certifications in Snowflake, Python, Tableau, dbt, or other relevant BI tools.
Prior consulting experience on data and analytics projects.
Familiarity with fast-paced, high-tech environments.

Responsibilities

Lead BI and analytics engagements from discovery through delivery, serving as the primary technical and strategic point of contact for the clients you support.
Run discovery sessions, stakeholder interviews, and workshops to surface business priorities and translate them into analytics requirements.
Partner with clients to define project scope, success criteria, deliverables, and timelines.
Build and maintain trusted advisor relationships with client stakeholders, from in-house analytics teams to executive sponsors.
Translate business questions into data and analytics solutions that drive decisions.
Develop dashboards, reports, and analyses using tools like Tableau and Sigma.
Conduct deep-dive data analysis and present findings to client audiences, including executive readouts, quarterly reviews, and strategic planning sessions.
Provide ongoing advisory support on measurement strategy, KPI definition, and analytics roadmap planning.
Manage scope, timeline, quality, and client communication across multiple concurrent engagements.
Identify expansion opportunities within active accounts and partner with the SVP of Analytics & AI to scope follow-on work.
Set the technical direction for client engagements and contribute to practice-wide delivery standards.
Design and implement data architectures for clients using Snowflake, BigQuery, dbt, and modern data stack tools.
Build and maintain ETL/ELT pipelines using Python, SQL, and orchestration platforms.
Drive data architecture recommendations and establish best practices across engagements.
Develop and refine reusable templates, frameworks, and assets that improve delivery speed and quality across the practice.
Stay current on tooling, techniques, and AI/ML approaches relevant to client work, and bring those forward into engagements where they create value.