Staff Data Engineer
New
J
JobgetherB2B SaaS
Based in United StatesFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 7+ years
- Required Skills
- PythonSQLKafkaSnowflakeBigQuerydbtDatabricks
Requirements
- 7+ years of hands-on data engineering experience with depth in streaming and event-driven architectures.
- Proven experience building production-grade near real-time pipelines using Kafka, Kinesis, Flink, Debezium, or CDC.
- Hands-on production experience with vector databases and embedding infrastructure such as Pinecone, Weaviate, pgvector, or Milvus.
- Advanced proficiency in SQL and Python.
- Working knowledge of cloud warehouse or lakehouse platforms like Snowflake, BigQuery, or Databricks.
- Experience with dbt for transformation layers.
- Experience contributing to end-to-end production data environments as an autonomous hire.
- Familiarity with B2B SaaS data models including customer lifecycle, ARR, and CAC.
- Working knowledge of BI and reporting tools like Looker, Tableau, or Power BI.
- Familiarity with ELT/ETL integration tools such as Fivetran or Airbyte.
- Strong understanding of data observability, lineage tracking, and monitoring.
- Experience using AI-augmented development tools like Claude or Copilot.
Responsibilities
- Design, build, and own near real-time data pipelines using CDC, streaming ingestion, and event-driven architectures.
- Evaluate, implement, and maintain vector database infrastructure and embedding pipelines for RAG and AI agents.
- Build scalable ELT and ETL pipelines from internal platforms, financial systems, CRM, and HRIS sources.
- Partner with leadership to architect warehouse or lakehouse systems of record.
- Develop lightweight transformation layers using dbt for reliable business datasets.
- Own data pipeline reliability, observability, lineage tracking, and automated failure alerting.
- Implement data governance practices covering documentation, access controls, and quality standards.
- Collaborate with Finance, Marketing, Customer Success, and Operations to translate requirements into data products.
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