Tango is a fast-growing startup focused on automating busywork and improving workflows. As the first Senior Analytics Engineer, you will design and maintain data pipelines, collaborate across teams, and enhance data-driven decision-making processes.
Responsibilities:
- Partner with product, business, and engineering teams to identify data needs, define metrics, and enable data-driven decision making
- Design, build, and own scalable data pipelines and data models that power analytics, reporting, and product insights
- Develop and maintain core datasets for financial and usage behavioral analytics, enabling deep understanding of customer behavior, product adoption, and business performance
- Help define, instrument, and validate product and business metrics, including those exposed in customer-facing dashboards
- Drive improvements in data quality, reliability, and observability, including ownership of SLAs for critical pipelines
- Collaborate with engineering teams to evolve data models alongside product changes
- Contribute to the definition of our analytics layer (e.g., dbt models, semantic layer, metric definitions)
- Create and maintain clear technical documentation and data contracts
Requirements:
- 5+ years of experience in data-focused roles (Analytics Engineering, Data Engineering, or similar)
- Strong experience modeling data for analytics, especially financial metrics and user behavior / product usage data
- Advanced SQL skills and experience designing well-structured, scalable data models
- Experience building and maintaining data pipelines in modern data stacks
- Hands-on experience with Snowflake, Redshift or BigQuery and familiarity with dbt (strong plus)
- Experience with cloud platforms such as AWS, GCP, or Azure
- Proficiency in at least one programming language (e.g., Python or R)
- Experience with BI tools such as Tableau or Looker and AI Analytics platforms like Hex
- Strong analytical mindset with the ability to investigate data inconsistencies and ensure data integrity
- Ability to communicate effectively with both technical and non-technical stakeholders and translate business needs into data solutions