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Senior Data & Analytics Engineer II Kickstarter PBC. 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.