Data Engineering Jobs with Salary ≥ 100k

Summary: Candidates must possess significant experience, including over 7 years in Business Intelligence and Data Warehousing, and over 5 years with cloud solutions like AWS and big data technologies such as Spark and Kafka. A Bachelor's degree in a related field is required, along with at least 2 years of experience managing data engineering teams and expertise in SQL, data modeling, and ETL processes.

Skills: data governance, data integrity, data engineering standards, data integration, qa best practices, data analytics platform, etl, data architecture, aws, sla management, software engineering best practices, mlops, rdbms, sql, query optimization, team leadership

Summary: Candidates must possess a Bachelor's degree or equivalent experience, with a minimum of 7 years in data engineering/IT/Project Management, including at least 2 years in management. Required technical experience includes 4+ years with data sets, Hadoop, Spark, ETL tools, AWS stack, or Snowflake, alongside 2+ years focused on AI engineering concepts like RAG and Prompt Engineering.

Skills: data engineering, ai, it architecture, software development, agile, data architecture, data pipelines, cloud, architecture, product strategy, stakeholder management, performance evaluation, hipaa compliance, disaster planning, hadoop, spark

Summary: Candidates must possess a university degree in a data or technology-related field or equivalent experience, along with over five years of experience managing technical teams and significant hands-on experience in related technologies like Python, advanced SQL, ETL/ELT, and cloud data systems. Strong expertise in data quality, governance, and designing resilient, scalable data solutions is essential.

Skills: data engineering, data operations, data quality, system integration, data pipelines, etl, cloud platforms, snowflake, gcp, azure, devops, data reliability engineering, data modeling, python, sql, leadership

Summary: Candidates require a Bachelor's degree and 5+ years of related experience, or equivalent experience demonstrating applicable knowledge and scope. Desired technical knowledge includes ETL, SQL, relational/non-relational databases, cloud data processes, and enterprise-level data governance monitoring.

Skills: data platform management, infrastructure management, mentoring, data pipelines, etl, elt, data governance, data quality, data integrity, master data management, metadata strategy, business intelligence, analytics, sql, agile software development, scrum

Summary: Candidates must have 10+ years of professional experience in data engineering, with at least 5 years in leadership roles, demonstrating success in leading engineering teams and delivering large-scale projects. Essential technical expertise includes strong SQL, database design, and experience with a scripting language like Python or Node, alongside a strong understanding of production service nuances.

Skills: sql, database design, python, node, observability, alerting, change management, data warehousing, identity resolution, attribution infrastructure, system architecture, leadership, mentorship, project management, data engineering, analytics

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 7+ years of progressive software development and data solution development experience, including 2+ years leading high-performing teams remotely. Essential technical expertise includes designing and operating large-scale ETL/ELT data pipelines, especially with real-time streaming (Kafka) and batch processing in high-volume data environments.

Skills: data engineering, high-availability solutions, real-time streaming, kafka, etl workloads, data pipelines, data warehouse architecture, change data capture, scrum, python, go, php, redshift, aurora, dynamodb, aws