Sr. Data Engineer - Benepass

TEAM & ROLE

Benepass is hiring a Senior Data Engineer to build and scale the data platform that powers analytics, product reporting, and operational intelligence across the company. You will work on a small, high-impact data team that owns the warehouse, pipelines, transformation layer, semantic layer, and the infrastructure that makes data accessible, trustworthy, and fast.

This is a hands-on senior IC role. You will own the architecture and implementation of data systems from the current 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. You will take ownership of data quality, performance, cost, and the developer experience of everyone who works with data at Benepass.

YOU WILL

Build and Scale the Data Platform

• Own the design and implementation of 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 into the warehouse with correct handling of PII, multi-tenancy, and data residency constraints.
• Maintain and evolve orchestration and job scheduling infrastructure so pipelines run reliably, recover gracefully from failures, and surface clear observability when things break.
• Design the platform for performance and cost: query patterns, materialization strategies, incremental models, partitioning, and warehouse scaling/tuning.

Own Data Quality, Governance, and Correctness

• Build data quality checks, automated testing, and validation into every pipeline and model so bad data gets caught before it reaches stakeholders.
• Define and enforce patterns for data grain, slowly changing dimensions, multi-tenant access controls, and row-level security.
• Work with Engineering on source data contracts, instrumentation gaps, and schema evolution so upstream changes don't break downstream reporting.
• Maintain clear lineage, documentation, and metadata.

Enable Self-Service Analytics and Product Reporting

• Design and build dbt models that codify business logic, dimensional models, and metrics in version-controlled SQL.
• Author and maintain Cube models and measures so governed metrics serve cleanly into BI tools and internal dashboards.
• Implement row-level security patterns in Cube so multi-tenant data stays isolated.
• Optimize for query speed: pre-aggregations, caching strategies, and materialized views.

Partner Across the Company

• Product & Engineering: Work with Product on internal tools and reporting; partner with Engineering to instrument events and define schemas.
• Customer Operations, GTM, and Finance: Support CX with implementation and utilization metrics; provide GTM with pipeline and retention data; work with Finance on revenue, margin, and billing requirements.
• Data Scientists and Analysts: Build the foundation analysts depend on, providing documentation, training, and support.

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.

OUR TECHNOLOGY STACK

AWS (Aurora/RDS, DMS, Redshift Serverless, S3, Lambda), dbt, Cube, Airbyte, Python, SQL, Metabase, Terraform, Docker, Kubernetes.