Top Entry Level Data Engineer Jobs

Processes and troubleshoots IRIG 106 flight-test data, coordinates with instrumentation, operations, IT, and leadership, and resolves data-recovery issues. Supports IT assignments, identifies processing needs and cost-saving solutions, and receives training in data processing, server administration, and desktop support. Requires strong analytical, troubleshooting, communication, and independent-work skills, with occasional extended hours and weekends.

Lead technical strategy for commercial data governance, data risk management, and reliability of data-driven workloads. Provide architecture, design, development, and testing leadership while mentoring engineers and driving technical decisions. Partner with stakeholders to scale governance practices, improve delivery processes, and build trusted data solutions using cloud platforms, Spark, Python, SQL, and data-lakehouse architectures.

Design and operate end-to-end infrastructure for intracortical neural data, from ingestion and validation through preprocessing, versioned dataset releases, and training data loaders. Preserve scientific metadata and provenance while implementing signal processing, quality checks, storage optimization, parallel processing, and monitoring. Collaborate with neuroscience, machine learning, infrastructure, and IT teams to support scalable, reliable model training and sensitive research-data management.

Build governed data models, tested analytics transformation layers, ETL pipelines, semantic datasets, and AI-ready data workflows. Partner with analysts, developers, and domain experts to define metrics, schemas, documentation, lineage, and validation frameworks. Support ML features, retrieval pipelines, unstructured data, and LLM output benchmarking while applying software engineering, compliance, and performance standards.

Develop and maintain enterprise geospatial applications, web services, dashboards, geoprocessing tools, and automation using Python and Esri technologies. Integrate and administer geospatial systems, troubleshoot GIS applications, manage data gateways, and migrate legacy applications to AWS Cloud One. The role also requires ArcGIS administration, database experience, spatial data standards knowledge, technical communication, and support for Air Force GeoBase initiatives using Agile practices.

Designs and builds lakehouse data models, pipelines, data marts, semantic layers, and digital-twin integrations for power plant assets. Combines telemetry, financial, operational, maintenance, and reliability data using Azure and Databricks technologies. Develops visualizations and supports web-product delivery while collaborating with product managers, UX designers, software engineers, analysts, plant staff, and executives.

Design and implement scalable data infrastructure, pipelines, integrations, and models. Build automated insurance reporting pipelines, unify siloed datasets, enable self-service analytics, and enforce governance, quality, security, testing, and reliability standards. Partner with Risk, analytics, product, and cross-functional teams to deliver data solutions using modern data engineering tools and practices.

Lead delivery for analytics and data platform initiatives as a hands-on player-coach. Build and refine Bronze-to-Silver-to-Gold Medallion transformations, develop performant data models, improve pipeline reliability, and implement complex Spark and Databricks solutions. Coordinate contractors, plan sprints, review pull requests, manage dependencies, and communicate progress and risks. Support a GDPR-first, multi-region data strategy and collaborate with engineering leadership on data warehouse unification.

Leads end-to-end data engineering solutions, including scalable Snowflake and dbt pipelines, data modeling, SQL transformations, CDC, ELT, quality frameworks, monitoring, and production deployment. Designs engineering standards, troubleshoots complex data issues, improves performance and cost efficiency, collaborates with stakeholders, leads technical reviews, and mentors engineers.

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

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.

Build and scale foundational data infrastructure for AI-powered hardware and consumer devices. Responsibilities include developing reliable pipelines, integrating manufacturing, telemetry, quality, and customer operations data, creating durable data models, enabling manufacturing and product intelligence, and establishing data engineering standards. The role requires strong architectural judgment, distributed systems knowledge, production infrastructure experience, and cross-functional collaboration in an early-stage organization.

Supports secure data operations within the SUNet environment, including ingestion, storage, transfers, validation, documentation, access management, troubleshooting, and data lifecycle activities. Maintains inventories and technical records, coordinates Change Requests and user onboarding, manages cloud and on-premises storage, and develops scripts for automation. The role supports Department of Defense AI and autonomy initiatives while collaborating with government, cybersecurity, engineering, vendor, and AI/ML stakeholders.

Designs and maintains reliable data pipelines and processing systems for high-volume telecommunications and digital evidence data. Builds data infrastructure supporting AI models and analytics, develops schemas and data models, optimizes performance and scalability, and monitors data quality. Collaborates with AI, data science, and software engineering teams, documents architecture, and follows secure practices for sensitive law enforcement data.

Investigate difficult public and municipal data sources, reconstruct and validate datasets, design data models, and build production systems for acquiring and refreshing data. The role partners with operations and state expansion teams, prototypes solutions, reviews TypeScript, uses PostgreSQL and spatial data, and owns reliable production implementations supporting affordable housing site selection.

Develops analytics solutions for learning and assessment data, including ETL scripts, data models, machine learning models, statistical analysis, and Power BI dashboards. Partners with faculty to gather requirements, generate insights, analyze student learning behavior, and deliver descriptive and diagnostic analytics. Provides dashboard training, consultancy, and knowledge transfer to staff while applying generative AI and large language model techniques.

Build and maintain production data ingestion pipelines, transformation models, warehouses, and schemas for PerfectServe’s data platform. Deploy reliable, tested pipelines using idempotency and data-quality checks, apply dimensional and medallion modeling, participate in design reviews, and document owned pipelines and runbooks. The role requires SQL, programming experience, dbt or similar transformation tools, cloud data warehouse experience, ELT orchestration, AWS knowledge, and a strong data-testing mindset.

Designs, deploys, and optimizes production-grade data infrastructure, ETL/ELT pipelines, distributed databases, streaming systems, vector databases, APIs, and feature stores for AI and machine learning applications. The role also implements data quality, observability, lineage, testing, compliance, and secure cloud-native deployments. As a forward-deployed engineer, the employee partners directly with customers, prototypes solutions, transitions them to production, and feeds field learnings into platform improvements.

Own Qualitate’s data engineering foundation by building pipelines for structured and unstructured data, designing relational and non-relational data models, and developing AI-powered enrichment workflows. Establish data quality monitoring, integrations, schemas, contracts, lineage, and documentation. Partner with Applied AI, Research, and Engineering while shaping the company’s early data architecture and enabling customer data integrations.

Build and scale data insights capabilities, write SQL queries in Snowflake, transform data with Python, and create dashboards in Qlik Sense and Tableau. Support data governance, preprocessing pipelines, predictive modeling, documentation, version control, and automated data workflows. Collaborate with Data Engineering teams to deliver real-time insights for robotics and supply chain automation projects.

Lead the design and development of Azure-based big data solutions, including batch and real-time ETL/ELT pipelines, data architecture, ingestion, curation, warehousing, and storage. Build solutions using Azure Data Factory, Databricks, Synapse, Spark, Python, SQL, and related Azure services. Support Salesforce migration and integration while applying strong RDBMS, data management, and big data design expertise.

Designs, builds, and maintains university data pipelines, integrations, data models, and analytics-ready datasets. Transforms operational data into trusted, governed assets; implements data quality checks, documentation, security, and access controls; and supports reporting, self-service analytics, AI-ready workflows, and institutional decision-making. Troubleshoots platform issues, automates manual processes, and partners with technical and university stakeholders to improve data delivery.

The FSR Engineer supports a government data platform onsite in Washington, DC. Responsibilities include configuring and troubleshooting data feeds, developing Spark transformations and datasets, monitoring builds, designing platform applications, testing with subject matter experts, responding to data inquiries, and collaborating with product teams to debug platform code.

Builds and supports data feeds, transformations, datasets, and platform applications using Spark and programming languages. Troubleshoots missing data, build failures, and application issues; monitors development progress; responds to data-footprint inquiries; and collaborates with technical teams and subject-matter experts in an onsite government-contract environment.

Build and maintain Google Cloud data pipelines and ETL/ELT processes supporting GenAI and RAG solutions. Classify and govern sensitive data using Dataplex and Cloud DLP, enforce access controls, assess AI readiness, and design reusable schemas and data models. Collaborate with engineering, delivery, and client teams to integrate legacy systems, monitor pipeline reliability, conduct data quality testing, document data flows, and proactively identify compliance and delivery risks.