Top Entry Level Data Engineer Jobs

Leads data engineering for a functional area by defining future-state architecture, establishing technical standards, mentoring team members, developing code and procedures, fixing bugs, maintaining quality, gathering requirements, and supporting application functionality. The role serves as a subject matter expert, partners with architecture and application engineering leaders, and drives technology adoption and professional development.

Build and support data pipelines, API integrations, n8n automation workflows, internal web applications, dashboards, and AI-enabled business tools. Use Python, SQL, PHP, Snowflake, AWS, and related technologies to connect enterprise systems, transform data, troubleshoot production issues, and improve process automation. Responsibilities include authentication, data quality validation, technical documentation, monitoring, and collaboration with business stakeholders across finance, operations, sales, and IT.

Designs enterprise database architectures and scalable data solutions across relational, NoSQL, cloud, and on-premises environments. Responsibilities include data modeling, database performance optimization, security, disaster recovery, cloud migration, automation with Python, and support for AI/ML, vector database, and RAG architectures. The role also provides technical leadership, documentation, architecture reviews, troubleshooting, and team mentorship.

The ClaimsXM Data Engineer III role has limited responsibilities listed. The position requires U.S. citizenship or naturalized citizenship and requires all work to be completed within the continental United States, Alaska, or Hawaii.

Develop and deploy data pipelines and platforms that organize disparate structured and unstructured data. Responsibilities include data acquisition, preparation, database architecture, ETL development, cloud-based data and machine learning pipelines, containerization, deployment, and MLOps ecosystem development. The role collaborates with data engineers, developers, and data consumers in an agile environment and requires Top Secret clearance.

Design, develop, and support Denodo virtual views and data services; integrate Denodo with databases, APIs and cloud platforms; build reusable semantic/logical layers; optimize queries and performance; collaborate with architects and business teams; document designs, test, review code, deploy, and provide production support.

Own and improve ETL pipelines, investigate and validate analytics systems, optimize performance, cost, and availability, and design scalable data platforms. Collaborate with data scientists to deliver dashboards, insights, and client-facing data pipelines using strong software engineering practices.

The Data Engineer will clean, manage, and analyze datasets, automate ETL processes, and collaborate with traders and software engineers to derive insights.

The DMS Data Engineer is responsible for analyzing raw data, building data systems and pipelines, conducting complex data analyses, and collaborating on projects to enhance data quality and reliability.

Build and operate scalable streaming and batch data pipelines for monetization, financial, and operational data. Develop canonical data models, reusable data products, data quality controls, observability, lineage, reconciliation, and auditability. Partner with Product Engineering, Finance, Accounting, Analytics, and GTM teams to define data contracts and deliver reliable platform capabilities. Lead technical designs, complex cross-functional projects, incident response, and engineering improvements.

Build and maintain reliable data pipelines, models, lakehouse layers, and analytics-ready datasets using cloud data platforms. The role includes data quality validation, lineage, access management, monitoring, troubleshooting, documentation, stakeholder collaboration, and support for governed enterprise data assets and future AI use cases. Exposure to Microsoft Fabric, Azure services, medallion architecture, Git, CI/CD, and government ICT requirements is advantageous.

Designs and manages large-scale structured and unstructured data pipelines in AWS. Builds RAG pipelines involving document ingestion, embeddings, indexing, and retrieval; orchestrates ETL/ELT workflows with Prefect and Apache NiFi; and optimizes Elasticsearch systems. The role supports analytics, ML, and application workloads while ensuring data quality, governance, lineage, security, observability, and reliability. Collaboration with architecture, ML, and application teams and troubleshooting distributed systems are also required.

Designs and maintains scalable data pipelines using Databricks, Spark, and cloud services. Builds transformation workflows, integrates APIs, databases, and cloud storage, and supports analytics and reporting. Implements data validation, monitoring, observability, governance, security, and automation. Collaborates with data science, analytics, and business teams to deliver reliable data solutions in AWS and other cloud environments.

Designs and maintains cloud-based data pipelines and architectures, primarily using Snowflake. Builds ETL/ELT processes, automates reporting, supports predictive analytics and machine learning initiatives, and helps migrate legacy platforms to the cloud. The role involves data modeling, governance, troubleshooting, AI solution development, and collaboration with analysts, data scientists, actuaries, product managers, and insurance professionals. The position is onsite four days weekly with one remote day.

Support building and maintaining ETL/ELT data pipelines and cloud data workflows. Work with senior engineers and cross-functional teams to implement, test, deploy, and document data processes. Continuously learn cloud technologies, SQL, and Python/PySpark, follow coding best practices, and contribute to process improvements in an Agile environment.

Develop and design data pipelines, optimize data delivery and automate processes to support end-to-end solutions in various settings.

The role involves structuring and ingesting customer data, setting up ETL pipelines, and providing solutions for data export and import.

The Data Engineer will build and scale Baseten's internal data platform, designing data models and pipelines while enabling self-service analytics and ensuring data reliability.

This role offers training for aspiring Data Engineers, focusing on technology of choice, interview preparation, and skill enhancement with guidance on OPT STEM extension.

Design and maintain scalable data pipelines, models, curated datasets, analytics solutions, and manufacturing automation tools. Support Ignition and Sepasoft MES systems, integrate manufacturing software, and develop solutions for metrology and image inspection data. Apply software engineering practices and independently lead complex cross-functional projects from requirements through deployment.

Owns analytics datasets and data products for Netflix’s cloud games infrastructure. Builds scalable pipelines, data models, metrics, dashboards, tooling, and self-service assets from massive telemetry data. Partners with infrastructure engineering, data engineering, and data science teams to improve quality of experience, capacity, and utilization insights. Responsibilities include telemetry and logging, visualization, trustworthy documentation, scalable data solutions, and independently prioritizing high-impact investments.

Build and maintain data pipelines and models to provide high-quality data for reporting, BI, and analytics. Work with SQL, dbt, Python, cloud data stores, orchestration tools, and reconciliation systems to support analytics, BI tools, and data warehouse delivery.

Contribute to building and maintaining cloud-based data pipelines and data products. Develop, test, and optimize SQL and Python transformations, participate in code reviews, troubleshoot data quality issues, and follow CI/CD and documentation practices to support analytics and AI initiatives.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Build data extraction, transformation, loading, modelling, analytics, machine learning, and dashboard solutions for teaching and learning data. Partner with faculty to gather requirements, generate insights, analyze student learning behavior, and develop Power BI visualizations. Train and advise lecturers on dashboard use and provide knowledge transfer on analytics and modelling.