Data Engineer Jobs with Salary ≥ 100k

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 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: Candidates need a Bachelor's degree in Computer Science, IT, or equivalent experience, along with 3+ years of experience with big data technologies like Spark and cloud technologies like Databricks/AWS, and 3+ years working with RDBMS and SQL. Familiarity with the Healthcare industry, especially claims data, is preferred.

Skills: spark, databricks, etl processes, sql, data integration, data quality, data security, data pipelines, aws, azure, snowflake, rdbms, pl/sql, airflow, jira, healthcare

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 have 5+ years of experience as a Software or Data Engineer, proficiency in Java, and experience designing scalable ETL/ELT pipelines using Apache Spark (Scala or PySpark). A Bachelor's degree in a technical major or equivalent experience is required, along with an AWS Associate-level certification (or obtaining it within 12 months).

Skills: java, etl/elt data pipelines, apache spark, scala, pyspark, data modeling, warehouse patterns, lakehouse patterns, troubleshooting, debugging, scrum, automated testing, continuous integration, communication, remote work, aws associate certification

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

Summary: A Bachelor's degree or equivalent experience reflecting applicable knowledge and scope is required, along with a minimum of 5 years of related experience. Desired technical skills include proficiency in AWS, Snowflake, and Python.

Skills: data platform management, infrastructure management, mentoring, data pipelines, software development, etl, elt, data governance, data quality, master data management, metadata strategy, business intelligence, analytics, agile processes, code reviews, aws

Summary: Candidates must have 5+ years in Data or Software Engineering, including at least 2 years of direct people management experience, demonstrating expertise in Python, SQL, and modern data stack tools like dbt, Airflow, and BigQuery. A strong focus is placed on architectural ownership, pipeline mastery, and familiarity with revenue operations data schemas.

Skills: data engineering, software engineering, people management, ci/cd, testing, observability, python, sql, code review, terraform, infrastructure-as-code, mentoring, roadmap planning, etl/elt pipelines, dbt, airflow

Summary: Candidates must have a minimum of 8+ years in business intelligence and reporting, including 6+ years with MSBI software and 5+ years of MS SQL Server database experience, alongside extensive hands-on experience with Power-BI, ADF, and other Azure BI services. A foundational understanding of statistics, HIPAA protocols, and proven leadership/training capabilities are also required.

Skills: data engineering, business intelligence, microsoft bi platform, microsoft azure, etl, reporting, dashboards, data modeling, stored procedures, data quality, power-bi, ssrs, ssas, azure data factory, azure ml, hipaa

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

Summary: Candidates must have at least 7 years of experience in software development, enterprise application design, and big data processing. Leadership qualities and expertise in Scala, relational databases, and various data technologies are also required.

Skills: scala, big data engineering, sql, nosql, aws, elasticsearch, mongo db, spark, apis, node.js, kubernetes, docker, job scheduling workflows, git workflows, data transformation, leadership