Data Engineer Jobs with Salary ≥ 200k

Summary: Candidates must possess over 8 years of experience in data engineering, AI, ML platforms, or large-scale distributed systems, including hands-on experience integrating LLMs into production environments using methods like RAG and vector stores. A strong systems thinking approach, comfort working from ambiguity, and clear communication skills are also essential.

Skills: data engineering, ai systems, llm optimization, data science, large-scale data systems, ai agents, autonomous workflows, rag, vector stores, model evaluation, experimentation, data quality, observability, distributed systems, orchestration, fine-tuning

Summary: Minimum requirements include a Bachelor's degree in a related field plus 8 years of engineering experience, including 3 years of direct people management, with proven experience scaling data platforms for ML workflows and enforcing data quality frameworks. Preferred qualifications suggest 10+ years of experience, including 5+ years managing teams, and strong technical foundations in modern data stacks and Lakehouse architecture.

Skills: data platforms, machine learning workflows, data quality frameworks, slas, observability, databricks, spark, python, sql, aws, streaming systems, orchestration frameworks, distributed systems architecture, lakehouse architecture, devops, terraform

Summary: A Bachelor's degree or foreign equivalent in Computer Science, Electronics Engineering, or a related field is required, along with five years of experience in machine learning infrastructure and ML operations development within a health plan or provider organization. Candidates must also demonstrate expertise in using AWS or other public clouds for Data Engineering, data management/quality assurance, distributed systems engineering, and programming proficiency in R, Python, Java, and C#.

Skills: data engineering, provider data, data products, health outcomes, process efficiency, profitability, data assets, data quality assurance, ml-based tools, decision support tools, ml ops, governance, compliance, data security, aws, python

Summary: Candidates need 10+ years in data engineering, including at least 2 years managing teams in cloud-native environments, with deep expertise in modern data stack technologies like AWS, Snowflake, dbt, Airflow, Python, and SQL. Strong people management skills and a proven track record of designing and implementing data platforms are essential.

Skills: data pipeline design, performance optimization, aws cloud services, people management, data platform architecture, technical standards, scalable data pipelines, data governance, security control, quality control, aws data services, snowflake, dbt, airflow, airbyte, python

Summary: Candidates must be foremost engineers with at least five years of industry experience, demonstrating expertise in Python, SQL, high code quality, and engineering best practices like automated testing. They must also have experience managing three or more people and excel at communicating complex concepts to both technical and non-technical audiences.

Skills: python, sql, data pipelines, etl, data architecture, automated testing, design patterns, communication, mentoring, project leadership, ml/ai awareness, data engineering, strategy development, collaboration

Summary: Candidates must have 8+ years in data science, data engineering, or machine learning roles, including 4+ years in people or delivery management leading technical contributors. Proven experience managing cross-functional ML technical teams and driving delivery across multiple workstreams is required, along with a strong understanding of the data science lifecycle and practical agile experience.

Skills: people leadership, agile delivery management, cross-functional coordination, data infrastructure, medallion layers, data pipelines, llm operations, agentic products, rapid prototyping, iterative development, agile workflows, scrum, kanban, hiring excellence, data governance, productionization

Summary: The role requires 8-12+ years of experience in building production systems, with a strong preference for Python and familiarity with LLMs. Candidates should also have experience in cloud-native data platforms and a passion for reliability and explainability in regulated domains.

Skills: python, llms, agent frameworks, cloud-native data platforms, document ingestion, ocr, entity resolution, explainability, audit-grade accuracy, collaboration, streamlit, fastapi, langchain, data engineering, regulated industries