Top Entry Level Data Engineer Jobs

Develop and advance the enterprise data platform by gathering business requirements, designing data models and warehouses, and building data visualization solutions. Administer reporting, analytics, and visualization platforms while promoting best practices. Collaborate cross-functionally, apply business and process knowledge, manage multiple projects, communicate strategically, and provide technical leadership in data engineering and business intelligence.

Develop Python data pipelines and business data objects for applications. Build and deploy CI/CD pipelines, implement source-to-target mappings, resolve complex technical problems, interpret software design requirements, and perform unit and integration testing. The role also promotes software quality practices, cross-functional collaboration, and continuous improvement. It is onsite five days per week in Chicago or Peoria, Illinois.

Design, develop, maintain, troubleshoot, and document scalable data pipelines and infrastructure using AWS, Airflow, Snowflake, Git, and Azure DevOps. Collaborate with analytics engineering teams to support One Funnel and OneSite initiatives and enable data-driven business decisions. Apply SQL and Python to solve business challenges, improve system reliability, implement CI/CD and automated testing, and manage data engineering requirements in an Agile environment.

Develop data products, pipelines, and business intelligence reporting solutions using cloud technologies, large-scale databases, streaming systems, ETL/ELT, and data warehouses. Translate business requirements into technical solutions, analyze high-volume datasets, troubleshoot SQL and pipeline issues, support user acceptance testing, and identify insights, patterns, anomalies, and trends. The six-month co-op includes mentorship, leadership development, and project presentation opportunities.

Design, build, and deploy scalable data pipelines and schemas to ingest and normalize disparate, multi-classification data for high-fidelity simulations. Integrate APIs, manage databases and data warehouses, and maintain ETL/ELT processes to support modeling, simulation, and data science teams in mission-critical space operations.

Design, develop, and maintain scalable ETL/ELT pipelines using Python, PySpark, Snowflake, and Azure technologies. Build data ingestion frameworks, dimensional data models, and enterprise warehouse solutions. Optimize Snowflake workloads, implement data quality and governance controls, monitor production pipelines, troubleshoot incidents, document data processes, and collaborate with architects, analysts, data scientists, and business stakeholders. Support secure, compliant, reliable data platforms for analytics, reporting, and operational use cases.

Architects, develops, and optimizes scalable data pipelines and cloud-native platforms. Leads data engineers in integrating structured and unstructured data across secure environments, establishes engineering standards, ensures data quality, and supports analytics, reporting, and mission-critical decisions. Uses ETL, distributed systems, streaming architectures, cloud services, and access controls to deliver reliable enterprise data infrastructure.

Develops and operates integrated data systems supporting New Glenn certification and launch services. Responsibilities include ensuring data quality, maintenance, sharing, governance, customer engagement, training metrics, and certification support. The role collaborates with New Glenn teams, NASA, and U.S. Space Force partners; plans technical products, communicates results, enforces data-marking practices, manages resources, and provides product ownership in a fast-paced aerospace environment.

Develops and maintains scalable data ingestion, transformation, modeling, and warehousing pipelines. Supports ETL/ELT workflows, data quality validation, governance, documentation, cloud platform deployment, and compute/storage optimization. Collaborates with analysts, data scientists, and product teams, participates in Agile ceremonies and code reviews, and helps improve engineering processes using SQL, Python, and cloud technologies.

Build and maintain enterprise data pipelines and infrastructure supporting natural-language querying, semantic search, knowledge graphs, and GraphRAG for the SEC. Responsibilities include onboarding data sources, processing XBRL and financial filing data, managing metadata and lineage, validating data quality, monitoring pipeline health, resolving integration issues, and using AWS data services. The role also requires compliance with federal security standards and collaboration with the Data Architect.

Build and operate large-scale data pipelines, web-scraping systems, and data-collection tools for AI datasets. Maintain analytical databases, optimize queries, troubleshoot pipeline and data-quality issues, manage containerized workloads across Kubernetes and Linux infrastructure, and support CI/CD workflows. The role also involves documenting technical processes, researching improvements, and developing scalable APIs in a fully remote environment.

Provides technical leadership and hands-on expertise for enterprise data modernization. Designs scalable batch and real-time pipelines, modernizes legacy data stores and BI workloads, implements data quality and reconciliation controls, and enables governed data products using Azure, Databricks, Microsoft Fabric, and related platforms. Leads API- and event-based ingestion, metadata and lineage management, DevSecOps delivery, production support, architecture decisions, engineering standards, and team mentoring.

Own the end-to-end commerce data supply chain, including third-party ingestion, schema-drift detection, entity resolution, deduplication, freshness SLAs, and retirement of stale records. Design and operate an LLM validation fleet with evaluation harnesses, golden sets, escalation paths, and defensible unit economics. Build production data systems at scale, establish machine-checkable quality metrics, and work independently with the founder in a fast-moving, in-person environment.

Build and deploy governed data warehouse, BI, and data products supporting healthcare business workflows. Responsibilities include data acquisition, cleansing, validation, reconciliation, modeling, lineage, documentation, ETL/ELT, scalable pipelines, curated datasets, data quality, governance, and integration of multiple source systems. The role collaborates with product, application development, BI, security, architecture, and business teams to support dashboards, automation, AI-enabled workflows, agentic products, and decision support.

The Data Engineer role supports Sedgwick’s data engineering initiatives. The provided description contains limited role-specific information and does not detail responsibilities, required technologies, qualifications, travel, or clearance requirements.

Import, scrub, and manage client data; support reporting needs using SQL and BI tools; investigate error messages and system behavior; and provide customer-facing assistance requiring strong communication and professional acumen.

Designs, builds, and maintains scalable data pipelines, infrastructure, data models, dashboards, and visualizations. Collaborates with data scientists, analysts, and stakeholders to improve data quality, accessibility, security, and decision-making. Analyzes workflows and data patterns, contributes to coding standards, supports cloud data integration and warehousing practices, uses AI tools, and grows into deployment and support rotations.

Build and maintain reliable data ingestion, transformation, and modeling pipelines using Python, Dagster or Airflow, dbt, Snowflake, Airbyte, and AWS. Responsibilities include API and PostgreSQL extracts, Parquet and Snowflake loads, production monitoring, troubleshooting, backfills, incremental modeling, code reviews, Terraform updates, documentation, and runbooks. Partner with analytics and product teams on dimensional modeling, data quality, and pipeline reliability.

Build and maintain scalable batch and real-time ETL processes, data warehouses, lakes, and marts. Develop Power BI and SSRS reports and dashboards, automate data workflows, optimize queries, support secure data exchanges, protect sensitive information, and improve data quality and system stability. The role requires SQL and relational database expertise, collaboration with business users and data teams, and a focus on reliable, maintainable solutions. This is an entry-level campus recruiting position for May 2027 offers.

Develops and optimizes distributed genomic data pipelines using Hadoop, Spark, Hive, Impala, SQL, and ETL workflows. Integrates genomic, laboratory, epidemiological, and public health datasets from external repositories, ensuring data quality, governance, security, and reproducibility. Collaborates with bioinformaticians, epidemiologists, and laboratory scientists to support pathogen genomics, outbreak detection, surveillance, and situational awareness. Maintains version-controlled pipelines, documentation, data lineage, reports, and scientific communications. This is an on-site role in Atlanta with weekly team meetings.

Design, develop, and maintain Python and SQL data pipelines, ETL workflows, database architectures, and AWS cloud integrations. Create schemas, stored procedures, documentation, testing strategies, and secure scalable data solutions. Troubleshoot data quality and performance issues, participate in Agile delivery using Jira, conduct code reviews, and guide other data engineers while collaborating with technical and program stakeholders.

Entry-level rotational Data Engineer role focused on SQL Server development, SSIS package creation, data validation, ETL, performance tuning, and cross-platform integrations (Salesforce, Marketing Cloud). Participate in SDLC, code reviews, data profiling, reporting, and stakeholder meetings with the goal of permanent placement after rotations.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, and SQL. Supports SQL Server, SSIS, SSRS, and data warehousing environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, enables reporting and AI/ML workloads, and partners with business and IT teams to deliver technical data solutions.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, SQL, and Delta Lake. Supports SQL Server, SSIS, and SSRS environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, applies data warehousing practices, and partners with business users, analysts, and IT teams to deliver reporting, analytics, and AI/ML solutions.

Design, develop, test, and maintain ETL pipelines that integrate data from databases, APIs, and cloud sources into warehouses or lakes. Analyze schemas, implement transformations, troubleshoot data issues, collaborate with business and product owners, and deliver scalable software solutions. The role also involves optimizing pipelines as requirements and data volumes change, participating in peer review, and improving engineering processes.