Top Entry Level Data Engineer Jobs

The Data Engineer will develop cloud-based data pipelines, collaborate with clients on data architecture, and utilize engineering best practices to provide effective data solutions.

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.

Data Engineers at CapTech build and maintain data pipelines on cloud platforms, advise clients on technology usage, and ensure data architecture maximizes client value.

Data Engineer to develop data pipelines using AWS, Azure, or GCP, advise clients on cloud resources, and collaborate with stakeholders to enhance data architecture.

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

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

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

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.

Entry-level rotational Data Engineer role focused on SQL-based development, ETL (SSIS), data validation, profiling, performance tuning, and BI reporting. Work with Salesforce/Marketing Cloud integrations, develop and review SQL code, participate in SDLC, client meetings, testing, and cross-team deployments to produce reliable enterprise data solutions.

Design, build, and maintain data pipelines and systems to move, store, and serve production and test data. Act as SME for data architecture, collaborate on technology selection, and support integrations of COTS and custom tools for engineering, flight test, and business teams.

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

Design, implement, and administer operational data flows: ingest, clean, enrich, transform, secure, and move mission-critical data. Perform exploratory data analysis, troubleshooting, and collaborate with customers to ensure compliant, reliable data pipelines.

As a Data Services Engineer, you will support clients by developing data products and solutions, manage projects, and collaborate across departments.

The Snowflake Data Engineer will work on data cleansing and harmonization, moving from legacy systems to SAP S4, focusing on ETL/ELT development and view creation.

The AWS Data Engineer will work on a data cleansing and harmonization project, focusing on AWS ETL design and development using various AWS services.

As a Data Engineer at MAI, you will develop and maintain data platforms, design ETL/ELT processes, ensure data quality, and collaborate with teams to optimize data utilization for analytics and ML applications.

Develop ETL and data visualization solutions for a big data customer and partner experience transformation program. Build native Hadoop and Vertica ETL pipelines, collaborate with stakeholders, and support data integration and visualization deliverables for analytics teams.

Develop and maintain big data solutions using Hadoop, HBase, MongoDB and Core Java on Linux. Implement and support scalable data processing pipelines; contribute to big data architecture and platform tasks.

Remote entry-level Big Data Engineer role building data acquisition and munging pipelines. Requires strong Python3 (NumPy, Pandas), advanced SQL, scalable algorithm and data-structure implementation on distributed clusters, converting code to functional paradigms, and the ability to learn PySpark or Kafka quickly. Product-development, startup experience and real-world pipeline projects preferred.

Design, build, and maintain scalable ETL/ELT pipelines and data models to populate the data warehouse. Write reusable SQL and Python to transform data, monitor pipeline reliability and quality, enable AI workflows, collaborate with stakeholders, and drive adoption of modern data stack technologies (DBT, Iceberg, Databricks). Support data governance, observability, and security practices.