Top Entry Level Data Engineer Jobs

Develop and deploy Python data pipelines and business data objects for applications. Responsibilities include translating design requirements into implementations, building CI/CD pipelines, implementing source-to-target mappings, resolving complex technical issues, and conducting unit and integration testing. The role also promotes software quality practices, supports new program development, and collaborates cross-functionally on complex systems work.

Designs and implements scalable data, analytics, and AI solutions for clients. Builds and optimizes data models and ETL/ELT pipelines, performs testing and validation, translates business requirements into technical solutions, documents architectures, manages workstreams, supports client training, and contributes to governance, risk tracking, and solution design.

Build and maintain Databricks data pipelines using SparkSQL and PySpark; ingest, transform, model, and validate data from payer, provider, claims, platform, and API sources. Support automated file exchanges, dashboards, testing, data governance, access controls, and audit logging. Use AI coding agents responsibly, participate in design and code reviews, document technical decisions, and collaborate with business teams. The role is based in Miami and targets candidates graduating between December 2026 and June 2027.

Build and maintain Databricks data pipelines using SparkSQL and PySpark; ingest, transform, model, and validate healthcare data from payer, provider, platform, and API sources. Support automated file exchanges, dashboards, downstream workflows, testing, governance, access controls, and audit logging. Participate in design and code reviews, use AI coding tools responsibly, and progressively own data engineering work while handling protected health information.

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 maintain data pipelines, dashboards, reports, and SQL queries across ERP, WMS, e-commerce, and vendor systems. Monitor data integrity, reconcile inventory and EDI discrepancies, support pricing and rebate reporting, automate recurring reports, document business rules, and train branch and sales staff on self-service analytics. The role provides actionable insights for inventory, sales, purchasing, pricing, and distribution operations.

Designs, develops, and maintains scalable cloud-based data solutions, ELT/ETL pipelines, data warehouses, curated datasets, and transformation logic using Snowflake and related tools. Optimizes SQL and warehouse performance, monitors workflows, supports data governance and quality initiatives, documents data flows, and delivers trusted datasets for Tableau reporting. Collaborates with business and technical stakeholders in Agile processes while ensuring secure handling of sensitive PHI data.

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.

Build and operate production data pipelines, CDC streams, and Snowflake Bronze-layer infrastructure using Python, SQL, Redpanda, and Dagster. Maintain source adapters, historical snapshots, schema-drift detection, deduplication, data-quality checks, reconciliation, and observability. Collaborate on Silver-layer modeling, governance tagging, CI/CD, containerization, and HVR-to-Redpanda migration. Participate in incident response and develop deeper ownership of orchestration, streaming, data governance, and dbt-based layered modeling.

Builds and maintains ETL/ELT data pipelines and architecture for analytics. Responsibilities include monitoring jobs, troubleshooting loads, managing tickets, discovering and ingesting data from databases, APIs, files, and third-party applications, and transforming structured and unstructured data. The role also performs data modeling, quality audits, software testing, documentation, and agile project delivery while collaborating with technical teams and stakeholders.

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 and maintains scalable ETL/ELT pipelines and cloud data solutions using Azure Data Factory, Databricks, SQL, Python, and Java. Supports Databricks environments, Azure infrastructure, data quality, testing, CI/CD, monitoring, troubleshooting, and performance optimization. Integrates data for AI, machine learning, business intelligence, automation, and advanced analytics while collaborating with technical and business stakeholders.

Own end-to-end data workflows, including ingestion, storage, orchestration, transformation, validation, and delivery. Build ETL/ELT pipelines, manage cloud data platforms and transfers, optimize data models and queries, maintain data quality monitoring, troubleshoot production issues, and document data flows. Collaborate with technical and nontechnical teams to investigate discrepancies, support enterprise datasets, and apply secure data-handling practices.

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 maintain scalable data pipelines using Python, AWS, and Apache Spark. Process large datasets across multiple file formats, write SQL for PostgreSQL, Oracle, and MySQL, and develop unit tests. Support Linux and shell scripting workflows, with preferred experience in pipeline orchestration, EMR/Spark administration, Spark performance tuning, Java or Scala, cloud security, and release configuration management. This is a full-time onsite role in Tyson’s Corner, Virginia, requiring an active TS/SCI clearance with full-scope polygraph.

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.

Designs and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, and SQL. Supports SQL Server, SSIS, SSRS, Azure, data warehousing, reporting, analytics, and AI/ML workloads. Responsibilities include optimizing performance and reliability, implementing data quality controls, integrating enterprise data sources, modernizing platforms, and partnering with business and IT teams to deliver scalable data solutions.

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.

Build and manage data pipelines using Python, AWS, and Apache Spark. Process diverse file formats, query relational databases, write unit tests, and support Linux-based data environments. Preferred experience includes NiFi or Airflow, Java or Scala, EMR/Spark cluster administration, Spark performance tuning, cloud security solutions, and configuration management. The role requires an active TS/SCI clearance with polygraph and is based in Tysons, Virginia.

Designs, builds, and maintains scalable batch and streaming data pipelines 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 warehousing best practices, and partners with business and IT teams to deliver reporting, analytics, and AI/ML data solutions.

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.

Build and maintain Databricks data pipelines across bronze, silver, and gold layers. Ingest APIs, logs, billing exports, and reference data; implement attribution logic, governance, data quality monitoring, and cost optimization. Manage Unity Catalog permissions, lineage, refresh schedules, incremental processing, and CI/CD workflows while supporting multi-cloud storage and high-volume caller-identity data.

Participate in AHEAD’s associate development program, completing technical training, certifications, and project shadowing before supporting client data solutions projects. Responsibilities include collaborating across teams, solving complex problems, managing priorities, meeting deadlines, and developing consulting expertise. Candidates should have programming experience, SQL or NoSQL knowledge, willingness to travel extensively, and existing U.S. work authorization. Data platform and cloud experience are preferred.

Build and maintain full-stack data engineering solutions supporting BioIntelliSense’s remote patient monitoring and clinical intelligence platform. The role likely involves processing health-monitoring data, enabling analytics, and supporting reporting for clinicians, patients, and employers. Specific responsibilities and qualifications are not provided in the description.