Kickstarter is seeking an experienced Senior Data & Analytics Engineer II to join our Insights team. There is a tremendous opportunity to unlock new and valuable experiences for our community through the smart use of data.
As a Senior Data & Analytics Engineer II, you will play a foundational role in modernizing our data stack, building scalable and cost-efficient pipelines, enabling deeper insights across the organization, and architecting a data foundation that allows teams to leverage data towards our goals. You will work closely with product, engineering, and business teams to ensure Kickstarter’s data is high-quality, well-structured, and accessible for decision-making.
The salary range in this role in the United States is $175,000 - 191,300.
In this role, you will:
- Develop, own and improve Kickstarter’s data architecture—optimize our Redshift warehouse, implement best practices for data storage, processing, and orchestration.
- Design and build scalable ETL/ELT pipelines to transform raw data into clean, usable datasets for analytics, product insights, and machine learning applications.
- Enhance data accessibility and self-service analytics by improving Looker models and enabling better organizational data literacy.
- Support real-time data needs by optimizing event-based telemetry and integrating new data streams to fuel new products, personalization, recommendations, and fraud detection.
- Lead cost optimization efforts—identify and implement more efficient processes and tools to lower costs.
- Drive data governance and security best practices—ensure data integrity, access controls, and proper lineage tracking.
- Collaborate across teams to ensure data solutions align with product, growth, and business intelligence needs.
About You
- 8+ years of experience in data engineering, analytics engineering, or related fields.
- Strong experience with cloud-based data warehouses (Redshift, Snowflake, or BigQuery) and query performance optimization.
- Expertise in SQL, Python, and data transformation frameworks like dbt.
- Experience building scalable data pipelines with modern orchestration tools (Airflow, MWAA, Dagster, etc.).
- Knowledge of real-time streaming architectures (Kafka, Kinesis, etc.) and event-based telemetry best practices.
- Experience working with business intelligence tools (e.g. Looker) and enabling self-serve analytics.
- Ability to drive cost-efficient and scalable data solutions, balancing performance with resource management.
- Familiarity with machine learning operations (MLOps) and experimentation tooling is a plus.
- Strong problem-solving and communication skills—comfortable working cross-functionally with technical and non-technical stakeholders.