Senior Data Engineer
New
B
BenepassFintech benefits
U.S RemoteFull-TimeSenior
SalaryBase Salary $175,000 to 195,000 + equity
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Job Details
- Experience
- 5+ years of data engineering experience
- Required Skills
- AWSPythonSQLData engineeringdbt
Requirements
- Have 5+ years of data engineering experience, with growing ownership of platform architecture, data pipelines, and data quality.
- Write strong SQL and Python and have experience building production data pipelines on modern data stacks.
- Have hands-on experience with data warehouses such as Redshift, Snowflake, or BigQuery and transformation frameworks such as dbt.
- Have experience building and maintaining data replication pipelines using CDC, DMS, Airbyte, Fivetran, or similar tools.
- Understand dimensional modeling, data grain, slowly changing dimensions, and data quality patterns.
- Have experience with semantic layers or metrics platforms such as Cube, LookML, or MetricFlow and governed metrics in BI tools.
- Be comfortable with AWS data services such as RDS, S3, Redshift, DMS, and Lambda; Terraform is preferred for infrastructure-as-code.
- Have experience with PII, multi-tenant data, row-level security, and compliance requirements in regulated domains.
- Communicate technical decisions clearly to stakeholders across the company.
- Nice to have: experience with orchestration tools, real-time or streaming data, BI, observability or cataloging tools, Kubernetes, Docker, or data science workflows.
Responsibilities
- Own the design and implementation of the data platform, including its warehouse, replication, orchestration, transformation, and semantic layers.
- Build reliable pipelines that replicate production data into the warehouse while handling PII, multi-tenancy, and data residency constraints.
- Design and tune data systems for query performance, cost, recoverability, and observability.
- Build automated data quality checks, tests, and validation into pipelines and models.
- Define patterns for data grain, slowly changing dimensions, multi-tenant access controls, and row-level security.
- Build dbt models and Cube models and measures that provide governed metrics to BI tools, product reporting, and internal dashboards.
- Partner with Product and Engineering on data needs, event instrumentation, schemas, and production data pipelines.
- Support Customer Operations, GTM, and Finance with operational, sales, revenue, billing, and financial data.
- Improve platform reliability through monitoring, alerting, incident response, post-mortems, tooling, and documentation.
- Identify platform gaps and scaling bottlenecks, evaluate new capabilities, and align data infrastructure with broader AWS, Kubernetes, and security patterns.
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