Data Engineer Jobs with Salary ≥ 80k

Summary: A strong background in both data and software engineering with at least 2 years of relevant experience is required. Proficiency in programming languages and excellent debugging skills are essential.

Skills: data engineering, software engineering, python, sql, debugging, technical communication, retl tools, bigquery sql, airflow, dbt, django orm, product analytics, medallion architecture, technical writing, mentoring

Summary: Design and maintain data pipelines and infrastructure for BI and AI decisions; own data models for RCM, intake, and clinical ops; requires SQL, Databricks/Redshift, and Airflow/dbt.

Skills: sql, postgresql, mysql

Summary: 3+ years of data engineering experience with expert-level SQL skills, particularly in PostgreSQL. Experience in building high-throughput production data pipelines and a collaborative mindset are essential.

Skills: data engineering, sql, postgresql, etl pipelines, data quality, data infrastructure, data warehouse, analytics, collaboration, monitoring, documentation, elixir, aws, quicksight, ci/cd, high-throughput systems

Summary: The position requires a minimum of 2 years of experience as a professional software developer and familiarity with structured Software Development Methodologies, preferably Agile. Strong problem-solving and analytical skills, along with proficiency in programming languages such as Java, Scala, or Python, are essential.

Skills: data engineering, software development, problem solving, analytical thinking, java, scala, python, big data, spark, hadoop, kafka, databricks, snowflake, cloud technologies, azure, aws

Summary: The role requires 7+ years of experience in data engineering, particularly with high-volume consumer data in advertising. Candidates should have expert-level coding abilities in SQL and Python, along with experience in building high-throughput data pipelines.

Skills: data engineering, sql, python, etl processes, data models, data quality, monitoring systems, anomaly detection, collaboration, technical leadership, data visualization, dashboarding, advertising technology, machine learning, data infrastructure, problem solving

Summary: 5+ years of experience in technical project management or data coordination is required. Strong understanding of media data workflows and exceptional communication skills are essential.

Skills: technical project management, data coordination, vendor management, campaign reporting, communication skills, asana, jira, cross-functional coordination, data workflows, attention to detail, process improvement, data governance, hcp media, pharmaceutical marketing, agile certification, pmp certification

Summary: Candidates should have 3-5+ years of experience with Python and SQL in data-intensive environments. A B.S. in Computer Science or a related field, or equivalent experience is required.

Skills: python, sql, docker, kubernetes, typescript, rust, aws athena, apache airflow, software engineering, code readability, testing, sql databases, api development, blockchain nodes, decentralized tech

Summary: Candidates must have a Bachelor’s Degree and at least 7 years of experience in data engineering, with 5 years in data architecture. Preferred qualifications include a Master’s Degree and extensive experience with various programming languages and data tools.

Skills: data engineering, data architecture, python, java, go, sql, kafka, airflow, spark, aws glue, kinesis, databricks, snowflake, machine learning, mentoring, collaboration

Summary: Candidates must have 15+ years of experience in a data-centric role and be expert in Python and SQL. Hands-on experience with Snowflake and advanced knowledge of Tableau are also required.

Skills: python, sql, snowflake, tableau, data pipelines, data transformation, analytics workflows, product kpis, collaboration, data quality, business metrics, user behavior, feature usage, data models, stakeholder engagement, cloud platforms

Summary: Bachelor's degree with a minimum of 5 years of professional experience in data engineering is required. Candidates must have extensive experience in data engineering, analytics, and cybersecurity domains.

Skills: data engineering, analytics, cybersecurity, python, java, aws, sql, etl, data quality, data modeling, data architecture, data visualization, machine learning, cybersecurity tools, technical leadership, mentorship

Summary: Candidates must be authorized to work in the US and have a minimum of 2 years of relevant experience. Proficiency in programming languages and a strong understanding of data warehousing concepts are essential.

Skills: python, sql, java, data warehousing, etl processes, database management, data streaming, data pipelines, data orchestration, agile, cloud platforms, jupyter, open-source technologies, geospatial data transformation

Summary: A minimum of 6 years of experience in software or data engineering is required, along with proficiency in programming languages such as Python, SQL, and Java. Candidates should have a strong understanding of data warehousing concepts and experience with ETL processes and data streaming technologies.

Skills: python, sql, java, data warehousing, etl processes, database management, data streaming, apache kafka, aws kinesis, apache flink, apache spark, devsecops, github, agile, cloud platforms, geospatial data

Summary: Candidates must have a Bachelor’s Degree and at least 6 years of application development experience. Preferred qualifications include a Master's Degree and extensive experience with big data technologies and cloud computing.

Skills: data engineering, machine learning, distributed microservices, full stack development, java, scala, python, rdbms, nosql, cloud computing, agile engineering, data warehousing, unix/linux, streaming applications, data governance, infrastructure management

Summary: Candidates should have 8+ years of experience in data engineering, including 3+ years in a leadership role. Proven expertise in relevant technologies and a strong background in collaborating with analytics and BI teams is essential.

Skills: data engineering, leadership, airflow, dbt, python, kafka streaming, snowflake, api-based data ingestion, real-time streaming, data pipeline orchestration, data integration, analytics, ai assistance bot integration, devops, ci/cd practices, data governance

Summary: The role requires 8+ years of experience in designing and scaling data systems, with proven technical leadership in complex data ecosystems. Candidates should have deep experience with modern data stacks and strong collaboration skills.

Skills: data engineering, data architecture, sql, python, data quality, data integration, cloud-native warehouses, orchestration tools, transformation frameworks, collaboration, mentorship, data systems, real-time data products, apache kafka, analytics, machine learning

Summary: Candidates should have 3+ years of experience in data engineering or backend engineering with a strong data focus. A deep understanding of modern data architecture and strong programming skills in Go or Python are essential.

Skills: data engineering, backend engineering, data architecture, etl, elt, go, python, sql, aws, kubernetes, data security, data reliability, data visualization, data governance, blockchain, analytics

Summary: 4 years of hands-on experience building and maintaining data pipelines in a cloud environment is required, with a strong emphasis on Snowflake. Candidates should also have 2+ years of Python experience and strong SQL skills.

Skills: data pipelines, data quality, data modeling, python, sql, dbt, etl, elt, data integration, data observability, data governance, bi tools, cross-functional projects, fivetran, kafka, cloud environment

Summary: Candidates must have a mastery of an object-oriented programming language, preferably Java, and a modern scripting language, preferably Python. A bachelor's degree or equivalent in a related field is required, along with 5-7 years of relevant work experience.

Skills: software engineering, data engineering, leadership, mentoring, collaboration, java, python, sql, automation, cloud technologies, vendor management, agile methodologies, problem solving, system monitoring, technical escalation, data systems

Summary: A Bachelor's degree in a quantitative or business field is required, along with 5-7 years of related experience. Candidates should possess advanced proficiency in Databricks and experience in building scalable data pipelines.

Skills: databricks lakehouse, unity catalog, pyspark, sql, python, aws, data pipeline optimization, data modeling, healthcare data, databricks api, ci/cd, automation, data governance, data transformation, data validation, data quality

Summary: Candidates should have over 10 years of experience in senior engineering roles, with at least 5 years as a technical lead. Experience with compliance, particularly in FedRAMP or DoD environments, is also required.

Skills: technical leadership, cross-functional collaboration, mentorship, strategic impact, compliance experience

Summary: Candidates need 2+ years of related experience and a bachelor's degree in relevant fields such as Data Science or Information Management Analytics. Knowledge of programming languages and strong analytical skills are essential.

Skills: data engineering, data collection, data analysis, programming languages, scripting languages, microsoft office 365, business acumen, analytical skills, communication skills, critical thinking, attention to detail, math skills, problem-solving skills, insurance knowledge, self-motivated, organized

Summary: Candidates should have a Bachelor's Degree in a relevant field and 1-3 years of experience in Software Engineering or Data Engineering. Knowledge of Databricks, Spark, and experience with AWS services are preferred.

Skills: data engineering, big data, cloud-based analysis, data pipelines, aws, databricks, redshift, python, spark, data processing, data integration, data cleaning, data visualization, agile, linux

Summary: Candidates should have 3+ years of experience in data engineering roles and strong experience with ETL tools and cloud platforms. Familiarity with data visualization tools and performance tuning techniques is also required.

Skills: data engineering, etl tools, google cloud platform, performance tuning, data visualization, data pipelines, aws technologies, python, nosql databases, machine learning, data science, data warehousing, realtime streaming, bi tools, java