Top Entry Level Data Engineer Jobs in Miami, FL

Build and maintain Databricks data pipelines using SparkSQL and PySpark, integrating payer, provider, claims, platform, and API data. Develop transformations, models, file exchanges, dashboards, automated workflows, tests, and data-quality validations. Collaborate in design and code reviews, use AI coding tools responsibly, and protect PHI through governance, access controls, and audit logging.

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.

Build and support scalable data platforms, pipelines, warehouses, lakes, integrations, and transformation processes. Improve data quality, observability, testing, monitoring, security, and platform performance. Collaborate with analysts, data scientists, engineers, product teams, and business stakeholders to deliver trusted data solutions. Opportunities may include cloud data engineering, architecture, streaming, analytics engineering, governance, and technical leadership. This is a proactive talent community posting for future opportunities, with travel up to 10%.

Build and maintain scalable data pipelines, infrastructure, data models, and APIs using Python, SQL, Airflow, Snowflake, dbt, Terraform, and cloud services. Ensure data quality, security, observability, and reliability while collaborating with analysts, product managers, and engineering teams. Own aspects of the FastAPI data service, including design, deployment, monitoring, and performance. Handle sensitive healthcare data in compliance with governance standards.

Build and maintain data pipelines for network and operational telemetry, integrate DataMiner and other monitoring systems, create Power BI dashboards, configure monitoring metrics and alerts, automate reporting, analyze network performance and recovery events, and translate technical findings into actionable insights for technical and executive 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.

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 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 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.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Own production data ingestion pipelines for healthcare and benefits data, including normalization, mapping, validation, reconciliation, observability, and secure handling of sensitive information. Unify census data processes, scale the platform, improve identity matching, and build automation and TypeScript tooling. The role requires strong Python and SQL skills, production orchestration experience, reliable testing and data-quality practices, and effective communication with technical and nontechnical stakeholders.

Leads a data engineering team and supervises one Data Engineer while designing, maintaining, and optimizing reliable data pipelines and infrastructure. The role integrates internal and external data sources, supports SaaS analytics dashboards and connected platforms, improves data-store performance, and ensures infrastructure quality, security, and uptime. Requires proficiency in Python, databases, distributed processing, orchestration, AWS, and machine learning.

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.

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.

Build and manage end-to-end marketing data platforms on Google Cloud. Responsibilities include configuring GA4 and Google Tag Manager, creating BigQuery ingestion pipelines, managing Cloud Functions, Pub/Sub, IAM, and service accounts, developing LookML semantic models and dashboards, and orchestrating ETL and reverse ETL workflows. The role also supports CDP integrations, identity resolution, audience syncing, experimentation, data quality assurance, and lifecycle marketing analytics.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Develop reliable datasets supporting reporting, analysis, finance, revenue recognition, and company decision-making. Collaborate with Finance, Engineering, Sales, Product, and Marketing to understand data needs, improve manual workflows, validate data accuracy, and create scalable solutions using modern data tools.

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.

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.

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.

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