Staff Data Engineer
$167,000–$260,500 year
On-site · San Francisco, California, United States or Atlanta, Georgia, United States
Job Summary
As a Staff Data Engineer at Scribd, you will be responsible for designing and owning canonical analytical data models, implementing modern data lake orchestration patterns, and defining data modeling standards for enterprise metrics. You will collaborate across teams to develop robust data solutions, mentor other engineers, and influence technical decisions while owning end-to-end data systems. The role requires 8+ years of experience in data engineering, expertise in Databricks and SQL, and a strong understanding of data architecture. Ideal candidates will possess leadership skills and a passion for operational excellence.
Required Qualifications
- 8+ years of experience in data engineering, with a strong background in data architecture, data modeling, and distributed data systems.
- Deep expertise in Databricks, Delta Lake, Spark, and modern lakehouse technologies.
- Advanced SQL expertise required — including complex joins, aggregations, window functions, CTEs, query optimization, and reasoning about data at different levels of aggregation.
- Deep experience designing dimensional and analytical data models and owning metrics across domains (analytics, ML, APIs).
- Experience designing reliable transformation workflows that maintain consistent data and business logic across batch and streaming pipelines.
- Demonstrated ability to lead technical initiatives, set standards, and influence decisions across teams.
- Comfort owning systems end-to-end, including monitoring, reliability, and cost management.
- Excellent communication skills with the ability to translate technical trade-offs to both engineers and non-technical stakeholders.
Desired Qualifications
- Experience with subscription, payments, or large-scale consumer data domains.
- Familiarity with AWS data services (S3, Glue, EMR, Kinesis) and cloud cost optimization.
- Knowledge of streaming architectures (Kafka, Kinesis, or similar).
- Experience implementing data quality, governance, and observability standards at scale.
- Contributions to open-source projects or thought leadership in the data engineering community.
- Experience operationalizing data observability through Datadog or equivalent monitoring tools.
- Experience working with Analytics teams to understand their requirements and translate to data products and data solutions.
Additional Requirements
- Employees must have their primary residence in or near specified cities.
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