Sr. Data Engineer

Benepass; United States; Remote; USD 130,000 - 190,000; Full time

Job summary

Benepass seeks a Senior Data Engineer to build and scale the data platform powering analytics and product reporting. You will own the warehouse, pipelines, transformation layer, semantic layer, and data infrastructure, ensuring data is trustworthy, accessible, and fast.

You will work cross-functionally with Product, Engineering, Customer Operations, GTM, and Finance to understand data needs and implement pipelines and models that support those needs.

Qualifications

• Hands-on senior individual contributor role.
• Own architecture and implementation of data systems from foundation to scale.
• Collaborate with product, engineering, operations, GTM, and finance to meet data needs.
• Focus on data quality, performance, cost, and developer experience.

Responsibilities

• Design and build Benepass's data platform: warehouse, replication, orchestration, transformation, and semantic layer.
• Architect pipelines to replicate production data with PII and multi-tenancy considerations.
• Maintain orchestration and job scheduling for reliable pipelines with observability.
• Design for performance and cost with materialization, partitioning, and scaling strategies.
• Make pragmatic build-vs-buy decisions when tooling is mature.

Skills

Data modeling

Distributed systems

SQL

ETL/ELT

dbt

Cube (semantic layer)

Data governance

Cloud (AWS)

Team collaboration

Tools

Redshift Serverless

Airbyte

DMS

dbt

Cube

Looker/Metabase

Aurora/RDS

Job description

About Us

At Benepass we're making benefits easy. We believe people are the most important asset to any company. Traditional one-size-fits-all benefits packages no longer cut it in today's hybrid and remote-first environment. With Benepass, companies can tailor their benefits to the unique needs of their workforce.

Through our easy-to-use and highly customizable fintech platform, People teams can implement, administer, and track the benefits that meet employees where they are. Employers design their benefits and perks plan by setting a contribution amount and eligible spend categories. Every employee has their own individual definition of wellness and needs different things to help them be their most productive, fulfilled self.

Our Mission

Helping companies reimagine how companies take care of their people.

Our Investors

We are backed by leading investors, including Centana Growth Partners, Portage Ventures, Threshold Ventures, Gradient Ventures, Workday Ventures, and Clocktower Technology Ventures. To date, the company has raised approximately $75 million in equity capital.

Articles

• Founder Story - Jaclyn Chen

• Benepass Raises $40M Series B

Candidate Resources

• Benepass | Candidate Resource Page

• Benepass Listed on Inc. Magazine's Best Workplaces of 2023

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.

Your work enables data scientists, analysts, operators, and product teams to move fast with confidence. When this role is working, data is available when teams need it, queries run fast and stay cheap, definitions are clear and versioned, and the platform scales cleanly as the business grows.

This is a force‑multiplier role. You will build shared infrastructure, not one‑off reports. You will balance short‑term delivery with long‑term architectural integrity. You will bring strong opinions on tooling, patterns, and quality, while staying pragmatic about build‑vs‑buy trade‑offs and speed to value.

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 (Aurora/RDS, application databases) 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.

• Make pragmatic build‑vs‑buy decisions; favor leverage and speed over custom solutions where mature tooling exists.

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 so financial and benefits data stays correct under scrutiny.

• 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 so people know where data comes from, what it means, and when it was last refreshed.

• Partner with Security and Compliance on data residency, encryption, access controls, and audit requirements for regulated data.

Enable Self‑Service Analytics and Product Reporting

• Design and build dbt models (staging through marts) 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 (Metabase, Looker, etc.), product reporting surfaces, and internal dashboards.

• Implement row‑level security patterns in Cube so multi‑tenant data stays isolated and access controls reflect the business model.

• Optimize for query speed: pre‑aggregations, caching strategies, and materialized views that let stakeholders query large datasets interactively.