Data Engineer II
New
S
SamsaraConnected Operations Cloud
US except the San Francisco Bay Metro Area, NYC Metro Area, and Washington, D.C. Metro AreaFull-TimeMiddle
Salary101,745 - 153,900 USD per year
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Job Details
- Experience
- 2-3 years
- Required Skills
- PythonSQLArtificial IntelligenceETLMachine LearningSalesforceSnowflakeSparkdbtRedshiftDatabricks
Requirements
- 2-3 years of industry experience in data engineering, with significant experience building large-scale data platforms.
- Hands-on experience working with modern data technologies stack, such as Databricks, DBT, Redshift, RDS, Snowflake or similar solutions.
- Proficiency in Python and SQL, with experience in designing robust ETL/ELT pipelines.
- Experience orchestrating data workflows at scale and enabling machine learning or AI use cases.
- Strong understanding of data modeling, performance optimization, and cost-efficient infrastructure design.
Responsibilities
- Build and maintain ETL/ELT data pipelines in Databricks and Spark, ensuring data is ingested, transformed, and delivered reliably for analytics and AI use cases.
- Develop and evolve logical and physical data models to support reporting, experimentation, and advanced workflows (e.g., scoring models, signal generation).
- Implement monitoring, alerts, and testing for data quality, timeliness, and lineage to ensure trustworthy data delivery.
- Support workflow orchestration with Databricks Jobs, DBT, or equivalent scheduling tools to operate at scale.
- Contribute to data pipelines and tooling that support retrieval-augmented generation (RAG), vector integrations, or embedding workflows.
- Design and optimize bulk GenAI data pipelines in Databricks to support generative AI applications at scale.
- Partner with AI engineers and data scientists to enable experimentation, model training, and production-grade deployments.
- Develop frameworks for data ingestion, transformation, governance, and monitoring across CRM, sales, and revenue systems.
- Work with RevOps, sales, and customer success stakeholders to translate business needs into data requirements and stable technical implementations.
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