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Turnitin's Revenue Operations team is modernizing its analytics foundation by migrating to governed dbt models on Redshift and building a trusted, consolidated view of the business for Sales, Marketing, Customer Experience, and Finance.
You will own the models, pipelines, and tests that downstream teams depend on. This is not a ticket-taking role—you are expected to understand why a metric matters before modeling it and to flag when a requirement as stated will produce a technically correct but practically wrong number.
As an early-stage function, you'll have unusual influence over how the analytics foundation is built and scaled.
Key Responsibilities:
- Lead the migration of legacy Alteryx workflows into documented, tested dbt models on Redshift
- Design models with deliberate grain and clear lineage, building toward a certified set of marts for reporting
- Own orchestration, monitoring, and alerting in Dagster or Airflow, and manage the ingestion layer feeding it
- Keep Redshift fast and cost-effective through performance tuning and optimization
- Trace issues to root cause—whether in reporting, data model, or upstream business process—and route each to the right fix
- Build data foundations behind RevOps initiatives such as account-based marketing, customer health scoring, and predictive work like churn and lead scoring
- Use AI and LLM tooling thoughtfully in the engineering workflow and help the team form a view on where it belongs
You are a self-starter comfortable with ambiguity, leading with questions and not waiting to be assigned work. You translate between business and technical contexts—explaining data problems to Sales Directors in terms of quota and quota problems to engineers in terms of data models. You think in systems, looking upstream and downstream before proposing fixes. You are constructively skeptical, reconciling numbers before shipping them, and impact-focused on delivering the "so what?" not just the data. You take pride in clean code and documentation.
Requirements:
Essential:
- 6–8+ years in Analytics Engineering, Data Engineering, or Business Intelligence with substantial hands-on modeling responsibility
- Proven experience building and maintaining production-grade dbt projects, including testing and documentation
- Hands-on production experience with Dagster or Airflow
- Advanced SQL and real experience with Amazon Redshift (or Snowflake/BigQuery), including performance tuning
- Git-based workflow, code review, and CI as a normal part of your work
- Strong working knowledge of the Salesforce data model
- Python for data work and automation
Preferred:
- Experience with Alteryx, particularly in a migration context
- Experience with Airbyte, Dagster, dbt, or comparable ELT tooling
- Salesforce-specific data modeling experience
- Exposure to predictive modeling or machine learning workflows
- Experience with large customer engagement datasets (1M+ rows)
- Ability to build and maintain Tableau dashboards
