Senior Data Engineer, AI & Data Platform
New
J
JobgetherAI & Data Platform
Fully remote work opportunity available across the United States (excluding Alaska).Full-TimeSenior
Salary$115,000 - $135,000 USD
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLCloud ComputingAirflowSparkData modelingDatabricks
Requirements
- 5+ years of professional experience building and operating production data platforms and pipelines.
- Strong expertise with Databricks, Spark, or similar distributed data processing technologies.
- Advanced SQL skills and experience with data modeling, including analytical schemas, data quality, keys, and relationships.
- Proficiency in Python and experience with workflow orchestration tools such as Airflow, Dagster, or Databricks Workflows.
- Experience working with cloud data platforms such as Azure data services, AWS, or Google Cloud.
- Hands-on experience building infrastructure that supports AI/ML workflows, including feature stores, training pipelines, embeddings, and model serving.
- Familiarity with modern AI architectures such as RAG systems, vector databases, LLM integrations, and AI agent frameworks.
- Experience using AI-assisted engineering tools to improve development workflows, research, debugging, documentation, or architecture design.
- Strong understanding of data governance principles, responsible AI practices, security, and compliance requirements.
- Ability to manage ambiguity, prioritize effectively, and take ownership of projects from concept to completion.
Responsibilities
- Own data initiatives end to end, from raw data ingestion through transformation, validation, deployment, and operational handoff.
- Design, build, and maintain scalable, reliable data pipelines that integrate data from multiple enterprise systems and external sources.
- Develop and optimize AI-ready data platforms, including data marketplaces, feature stores, embedding pipelines, and real-time data-serving layers.
- Build and maintain data models with strong foundations in quality, governance, accuracy, and usability for analytics and AI workloads.
- Create and support integrations that enable AI applications and intelligent tools to securely access trusted data.
- Partner with data science, product, engineering, and business teams to operationalize machine learning models and AI solutions.
- Implement data governance practices, including access controls, compliance requirements, lineage tracking, monitoring, and quality frameworks.
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