Data Engineer Jobs

Summary: Candidates must be currently enrolled in a relevant college degree program and possess proficiency in Python for backend development, scripting, or automation. A strong foundation in data structures, algorithms, API design, and understanding of data pipeline concepts is required.

Skills: python, aws, elasticsearch, data processing, cloud workflows, data pipelines, aws lambda, influxdb, gcp tools, timeseries data, network telemetry data, kibana, git, ci/cd, agentic ai frameworks, llm-based automation

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 have 7+ years of software engineering experience, including 3+ years building production-grade full stack AI systems with hands-on experience in agentic AI solutions using LangChain/LangGraph. Proficiency in Python and TypeScript/JavaScript, strong cloud experience (Azure or Google Cloud), and practical DevSecOps knowledge are required.

Skills: agentic ai, langchain, langgraph, prompt engineering, python, typescript, javascript, api integrations, data ingestion, devsecops, ci/cd, automated testing, vertex ai, bedrock, microservices, rmf

Summary: Candidates must possess 12+ years of experience, with a strong technical background in data warehousing, dimensional modeling, and ETL frameworks, specifically deep expertise in AWS and GCP technologies. Proven leadership experience managing both onshore and offshore teams, coupled with excellent communication skills to bridge business and technical needs, is essential.

Skills: aws cloud, gcp, snowflake, etl, dbt, data lakehouse, python, pyspark, sql queries, qliksense, data modeling, data governance, team leadership, architecture design, cloud storage

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 have 5+ years of professional data engineering experience, deep expertise in distributed processing frameworks like Databricks/Spark, strong SQL and Python skills, and experience building data infrastructure for ML workflows, including feature stores and model serving. AI fluency, including experience with AI coding tools and LLM integration patterns, is required.

Skills: data engineering, ai infrastructure, databricks, spark, sql, python, data modeling, feature stores, llm integration, rag architectures, vector databases, data governance, airflow, dbt, azure cloud, ci/cd

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 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 possess over 10 years of enterprise application development experience, including at least 3 years of hands-on AI/ML experience in production environments. Deep expertise in Databricks technologies like PySpark and Delta Lake, along with experience building AI agents using AWS GenAI tooling, is required.

Skills: ai, data engineering, databricks, pyspark, delta lake, workflows, medallion architecture, sql, nosql, llms, openai, claude, langchain, aws genai, ci/cd, data quality

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: Candidates must possess strong Python engineering experience building data extraction and transformation workflows, including experience calling LLM APIs and crafting effective prompts for structured data extraction. A solid understanding of ELT patterns, experience with AWS S3 and PostgreSQL, and a strong data validation mindset are also required.

Skills: python, data extraction, transformation, llm api calls, prompt crafting, elt patterns, data pipeline architecture, aws s3, postgresql, json schemas, data validation, monitoring, alerting, data schemas, document processing, ai orchestration

Summary: Candidates should possess 5-8 years of back-end software development experience with a strong background in data engineering, analytics, and building data-driven products, including deep expertise in PostgreSQL and MongoDB. A fit requires the ability to translate business needs into technical solutions, advocate for long-term solutions while meeting timelines, and thrive as a self-starter in fast-moving environments.

Skills: typescript, node.js, postgresql, mongodb, apis, etl pipelines, looker, data architecture, data ingestion, database schema optimization, data validation, technical leadership, data modeling, ai/ml capabilities, back-end development, data engineering

Summary: Candidates must have experience leading engineers in a technical or team lead capacity, coupled with solid experience as a Software or Data Engineer owning production systems, particularly using Python (4+ years). Essential technical requirements include experience with modern cloud data warehouses, ETL/ELT pipelines, data modeling, and orchestrated data pipelines like Dagster or Airflow.

Skills: python, etl/elt pipelines, snowflake, databricks, dagster, dbt, aws, data modeling, system design, team leadership, coaching, mentoring, code review, observability, fastapi, django

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 must have experience in AI development or data engineering with successfully deployed projects, expertise in data processing, vector databases, and strong Python skills for AI pipelines. Familiarity with RAG systems, NLP, and generative AI models is highly valued.

Skills: python, etl pipelines, xml, data modeling, metadata enrichment, generative ai, vector databases, elasticsearch, qdrant, neo4j, apache airflow, docker, aws, rag systems, nlp, fastapi

Summary: Candidates must have a Bachelor's or Master's degree in a related technical field and 8+ years in data engineering/warehousing, including 3+ years in leadership, with proven experience in Snowflake and cloud data warehouse architecture. Strong knowledge of medallion architecture, BI tools like Tableau, and modern data pipeline technologies (dbt, Airflow) is essential.

Skills: snowflake, medallion architecture, tableau, data warehousing, data ingestion, data transformation, data modeling, data engineering, bi development, data governance, data quality, etl/elt, dbt, airflow, fivetran, ai/ml readiness

Summary: Candidates must have over 6 years of hands-on data engineering experience, including 3+ years of agile leadership, ideally within a regulated data environment. Required technical expertise includes deep practical experience with AWS services, Kafka, Iceberg, orchestration tools like Terraform/Prefect/dbt, Python, SQL, and knowledge of modern AI/ML tooling.

Skills: aws, redshift, athena, rds, postgres, s3, msk, glue, lake formation, kafka, apache iceberg, terraform, prefect, dbt, python, sql

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 should have over 5 years of experience in Data Engineering or related fields, with strong Python skills and a deep understanding of AI principles. Experience with cloud infrastructure and workflow orchestration is also required.

Skills: data engineering, ai principles, python, cloud infrastructure, automation, workflow orchestration, containerization, version control, communication skills, innovation, scalable solutions

Summary: Candidates should have 5+ years of experience in full-stack software engineering and data engineering, with a strong foundation in custom software development. Practical experience in building and deploying AI-powered prototypes or MVPs is also required.

Skills: full-stack software engineering, data engineering, ai prototyping, python, react, next.js, apis, cloud platforms, sql, data pipelines, elt, machine learning, nlp, aws, data modeling, collaboration

Summary: The role requires 4+ years of experience in data engineering with strong hands-on Snowflake experience and proficiency in SQL and Python. Familiarity with AWS services and ELT tooling is also essential.

Skills: data engineering, snowflake, ai solutions, machine learning, sql, python, aws, data modeling, data warehousing, elt, data quality, collaboration, cloud-native, observability, governance, automation

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: Candidates must possess proven expertise in architecting solutions using Snowflake and Kinetica, along with mastery in designing Property Graphs or RDF schemas for Knowledge Graphs to map enterprise entities. Strong DBA skills for cloud-native environments and deep knowledge of advanced ETL/ELT patterns are essential.

Skills: snowflake, kinetica, nosql, knowledge graph, llm agents, schema design, ontology design, database administration, performance engineering, etl/elt strategy, change data capture, streaming, vector databases, graph databases, dbt semantic layer, rbac