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Salary: USD 175,000 - 195,000 / annual
Benepass is a Series B fintech platform that helps companies customize and manage employee benefits. You will join a small, high-impact data team as a Senior Data Engineer to build and scale the data platform that powers analytics, product reporting, and operational intelligence across the company.
In this hands-on senior individual contributor role, you will own the architecture and implementation of data systems from 0→1 foundation to 1→N scale. You will partner with Product, Engineering, Customer Operations, GTM, and Finance to understand data needs, then design and build the pipelines, models, and platform capabilities that support those needs.
Key responsibilities include:
**Build and Scale the Data Platform**
- Design and implement Benepass's data platform: warehouse (Redshift Serverless), replication (Airbyte/DMS), orchestration, transformation (dbt), and semantic layer (Cube).
- Architect data pipelines that reliably replicate production data from Aurora/RDS and application databases into the warehouse, with correct handling of PII, multi-tenancy, and data residency constraints.
- Maintain and evolve orchestration and job scheduling infrastructure for reliable pipeline execution, graceful failure recovery, and clear observability.
- Design for performance and cost: query patterns, materialization strategies, incremental models, partitioning, and warehouse tuning.
**Own Data Quality, Governance, and Correctness**
- Build data quality checks, automated testing, and validation into every pipeline and model.
- Define and enforce patterns for data grain, slowly changing dimensions, multi-tenant access controls, and row-level security for financial and benefits data.
- Work with Engineering on source data contracts, instrumentation, and schema evolution.
- Maintain clear lineage, documentation, and metadata.
- Partner with Security and Compliance on data residency, encryption, access controls, and audit requirements.
**Enable Self-Service Analytics and Product Reporting**
- Design and build dbt models (staging through marts) that codify business logic and dimensional models in version-controlled SQL.
- Author and maintain Cube models and measures for governed metrics served into BI tools (Metabase, Looker, etc.).
- Implement row-level security patterns in Cube for multi-tenant data isolation.
- Optimize for query speed through pre-aggregations, caching strategies, and materialized views.
- Provide clear paths for analysts and data scientists to extend models and build reports independently.
**Partner Across the Company**
- Work with Product and Engineering to understand data needs, instrument events, and close data gaps.
- Support Customer Operations with implementation health, claims/CX metrics, and utilization data.
- Provide GTM/Sales with pipeline, conversion, and retention data.
- Work with Finance on revenue, margin, billing, and financial reporting; ensure financial data is reconcilable and audit-ready.
- Build foundations that analysts and data scientists depend on; provide documentation and training.
**Drive Platform Reliability, Performance, and Developer Experience**
- Own end-to-end reliability: monitoring, alerting, incident response, and post-mortems.
- Improve query performance and cost; identify slow queries and optimize models.
- Build tooling and automation to reduce toil: deployment workflows, testing frameworks, and observability.
- Maintain documentation, runbooks, and onboarding materials.
**Shape the Data Platform Roadmap**
- Identify architectural gaps, technical debt, and scaling bottlenecks.
- Lead evaluation and adoption of new platform capabilities: real-time data, streaming pipelines, CDC patterns, data catalogs.
- Advocate for platform investments that multiply team impact.
- Partner with the Platform lead to align data infrastructure with broader AWS/Kubernetes patterns.
You will be expected to attend company-wide on-site events three times per year.
**Requirements**
- 5+ years of data engineering experience with growing ownership of platform architecture, data pipelines, and data quality.
- Strong SQL and Python; experience building production data pipelines on modern data stacks.
- Hands-on experience with data warehouses (Redshift, Snowflake, BigQuery) and transformation frameworks (dbt).
- Experience building and maintaining data replication pipelines (CDC, DMS, Airbyte, Fivetran, or similar).
- Solid understanding of dimensional modeling, data grain, slowly changing dimensions, and data quality patterns.
- Experience with semantic layers or metrics platforms (Cube, LookML, MetricFlow).
- Comfort with AWS data services (RDS, S3, Redshift, DMS, Lambda) and infrastructure-as-code (Terraform preferred).
- Experience working with PII, multi-tenant data, row-level security, and compliance requirements in regulated domains (fintech, healthcare, benefits).
- Clear written and verbal communication; ability to explain technical decisions to non-technical stakeholders.
- Fit with a high-ownership, high-trust culture.
**Nice to Have**
- Experience with orchestration tools (Airflow, Dagster, Prefect, Mage).
- Real-time data, streaming pipelines, or CDC patterns (Kafka, Kinesis, Debezium).
- BI tools (Metabase, Looker, Omni, Superset, Tableau).
- Data observability and cataloging tools (Monte Carlo, Great Expectations, Atlan).
- Fintech, payments, benefits, or healthcare domain experience.
- Kubernetes, Docker, or containerized data workloads.
- Data science workflows: feature engineering, experimentation platforms, or ML pipelines.
