Sr. Data Engineer

Benepass

2d•$175,000 - $195,000•Hybrid

About The Position

At Benepass, we are making benefits easy and believe people are the most important asset to any company. Our mission is to help companies reimagine how they take care of their people. We are 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 where 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.

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) and how they serve governed metrics into BI tools.
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; you can explain technical decisions to stakeholders across the company.
Fit with a high-ownership, high-trust culture. We hire for skill and for how people work with others.

Nice To Haves

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 domains where data correctness and compliance are critical.
Experience with Kubernetes, Docker, or containerized data workloads.
Exposure to data science workflows: feature engineering, experimentation platforms, or ML pipelines.

Responsibilities

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.
Provide clear paths for analysts and data scientists to extend models, write safe queries, and build new reports without waiting for engineering.
Partner Across the Company to Deliver Data Solutions: Work with Product to understand data needs for internal tools, customer-facing reporting, and product analytics.
Partner with Engineering (Core Services, Client Apps, Platform) to instrument events, define schemas, and close gaps where product data doesn't land cleanly in the warehouse.
Collaborate on production pipelines that power product features: reporting surfaces, usage dashboards, and data-driven workflows.
Support Customer Operations with data for implementation health, claims/CX metrics, utilization, and operational quality.
Provide GTM/Sales with pipeline, conversion, and retention data that joins Salesforce with product usage and warehouse data.
Work with Finance on revenue, margin, billing, and financial reporting requirements; ensure financial data is reconcilable and audit-ready.
Build the foundation analysts and data scientists depend on: clean staging models, dimensional marts, and governed metrics.
Provide documentation, training, and support so engineers and analysts can extend models and write queries safely.
Collaborate on complex analyses where data engineering work unlocks science or speeds decision-making.
Drive Platform Reliability, Performance, and Developer Experience: Own the end-to-end reliability of data pipelines: monitoring, alerting, incident response, and post-mortems when things break.
Improve query performance and cost: identify slow queries, optimize models, tune warehouse configuration, and right-size resources.
Build tooling and automation that reduces toil: deployment workflows, testing frameworks, data refresh triggers, and observability.
Maintain clear documentation, runbooks, and onboarding materials so the platform is accessible to new team members and partners.
Introduce new tools and patterns when they compound platform leverage; balance experimentation with production stability.
Shape the Data Platform Roadmap: Identify architectural gaps, technical debt, and scaling bottlenecks; propose solutions with clear trade-offs and sequencing.
Lead the evaluation and adoption of new platform capabilities: real-time data, streaming pipelines, CDC patterns, data catalogs, or next-generation BI/semantic layers.
Advocate for platform investments that multiply team impact: better observability, faster development cycles, cleaner abstractions, and lower operational overhead.
Partner with the Platform lead to align data infrastructure with broader AWS/Kubernetes/infra patterns and security posture.