Sr. Data Engineer

Benepass provides a customizable fintech platform that helps companies administer and track employee benefits. The company is hiring a Senior Data Engineer to build and scale the data platform supporting analytics, product reporting, and operational intelligence, while owning data quality, performance, governance, reliability, and developer experience.

Responsibilities:

• 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
• 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
• 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
• 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
• 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
• 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

Requirements:

• You've built data platforms from the ground up or scaled them through significant growth
• You write production-quality SQL and Python; you're fluent in modern data stacks (warehouse, orchestration, transformation, BI/semantic layers)
• You have strong opinions on data modeling, testing, and quality, with pragmatism about when to cut scope and ship
• You design systems for correctness first, then performance, then cost; you care deeply about data integrity in regulated domains
• You ship incremental value while keeping long-term architecture coherent
• You operate at a senior IC level (P4): you take ownership of ambiguous problems and drive them to durable solutions
• You partner across Product, Engineering, Customer Operations, GTM, and Finance without needing a formal PM or project manager
• You mentor data scientists, analysts, and engineers; you raise the bar for how the company thinks about data
• You make build-vs-buy decisions that favor leverage and team velocity
• You communicate technical trade-offs clearly to non-technical stakeholders and advocate for platform investments that compound
• 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
• 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