Senior Analytics Engineer

Senior Analytics Engineer

Location: Austin, TX (hybrid)

Schedule: Full-time | Hybrid (2–3 days in office)

Work Authorization Notice:

At this time, we are unable to provide immigration sponsorship for this position. Candidates must have current, unrestricted authorization to work in the country where the role is based.

About Us

Togetherwork is a $250M recurring revenue SaaS business with more than 33 software applications serving over 12 vertical markets. We help communities, organizations, and businesses thrive by delivering purpose-built software that supports their missions and operations.

Headquartered on South Congress in Austin, TX, Togetherwork is scaling rapidly. We are customer-focused, execution-driven, and committed to operational excellence. Our teams value accountability, collaboration, and continuous improvement.

About the Role

Togetherwork is looking for a Senior Analytics Engineer who thrives at the intersection of data engineering and business intelligence. You will own the transformation layer of our data platform, taking raw data landed in our warehouse and building the clean, trusted, gold standard models that power self-service analytics across the organization.

This is a hands-on individual contributor role with real influence. You will work directly with product, engineering, data science, and business leaders to ensure the data our teams rely on is accurate, well-modeled, and built to scale.

What You’ll Do:

• Gold Standard Data Modeling: Design, build, and maintain dimensional data models in Redshift that serve as the single source of truth for analytics and reporting. Your models will power self-service in Sigma and PowerBI, built for business users, not just engineers.
• Data Transformation: Own the transformation layer end to end using dbt. Clean, enrich, and structure raw data into staging, intermediate, and mart layers following modern analytics engineering best practices.
• AI-Augmented Development: You will be expected to leverage AI coding tools as a core part of your workflow. This means using AI to accelerate model development, generate and validate dbt transformations, write tests, and explore data patterns faster. We expect our engineers to work with AI, not around it.
• Data Quality and Governance: Define and enforce data quality standards, testing frameworks, and documentation so stakeholders can trust and self-serve the data without engineering support.
• Cross-Functional Partnership: Translate business requirements from product, finance, and operations into reliable data models. You bridge the gap between technical implementation and business outcomes.
• Performance and Scalability: Continuously optimize queries, models, and pipelines for performance as data volume and organizational complexity grow.
• AI and ML Enablement: Build clean, well-structured datasets that directly support data science and AI/ML initiatives, reducing time spent on data preparation by the modeling team.

What You’ll Bring:

• 5+ years in analytics engineering or data engineering roles with direct ownership of data model design and delivery.
• Deep expertise in modern data modeling: dimensional modeling, star schema, and dbt are non-negotiable.
• Hands-on experience with cloud data warehouses: Redshift and/or Snowflake.
• Proven ability to translate business requirements into data models that non-technical stakeholders can use.
• You can present data architecture decisions to a VP as clearly as you can to a fellow engineer.
• Experience leading data projects end to end in small, fast-moving teams.
• Comfort working in agile environments where priorities shift and ownership is broad.

Preferred Qualifications:

• Experience in a multi-product SaaS environment.
• Hands-on experience building dashboards and reports in Sigma or PowerBI.
• Familiarity with data ingestion tools.
• Exposure to AI/ML workflows and feature engineering.

Who You Are:

You are a builder who takes pride in data that just works. You don’t wait for perfect requirements. You ask the right questions, make sound modeling decisions, and iterate. You care about downstream users as much as upstream pipelines. You are equally comfortable whiteboarding a data model with a data scientist and presenting reporting logic to a business stakeholder.

You have strong opinions about data quality and governance, but you know how to ship in a scrappy environment without sacrificing the foundations