Top Data Engineer Jobs in Tampa, FL

Maintain and monitor Snowflake data warehouse and ingestion pipelines. Troubleshoot file/load/data quality issues, operate SFTP/drop-zone/file automation, support scheduled loads, build dbt models and tables, document runbooks, and serve as backup for core warehouse operations while collaborating with engineering and IT.

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.

Own end-to-end data platform architecture across ingestion, transformation, modeling, and serving. Improve warehouse efficiency, access control, reliability, observability, orchestration, CI/CD, and developer experience. Lead cross-functional technical initiatives, build monitoring and alerting, mentor engineers, guide architectural decisions, and develop AI-powered data tooling. The role requires strong SQL and Python skills, production experience with Airflow, BigQuery, and containerized infrastructure, plus architectural ownership and stakeholder communication.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

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.

Leads the design, development, administration, and optimization of enterprise data pipelines and Microsoft Fabric data platforms. Responsibilities include ETL/ELT architecture, medallion layers, data quality, CI/CD, infrastructure as code, security, hybrid connectivity, disaster recovery, performance optimization, and troubleshooting. The role also mentors engineers, establishes technical standards, supports architecture reviews, and provides senior-level guidance for secure, scalable, reliable data platform implementations.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.

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.

The Data Engineer develops data pipelines, supports Agile teams, ensures data performance, advocates for lean practices, and collaborates across functions to deliver insights for federal customers.

As a Sr Data Engineer, you'll conduct feasibility analyses, develop applications, monitor processes, consult with users, and ensure compliance while serving as a mentor to junior analysts.

Design, build, maintain, and optimize Spark and Scala ETL/ELT pipelines on AWS for terabyte- and petabyte-scale datasets. Work across S3, Snowflake, Kafka, and monitoring systems, resolve production data-quality and performance issues, write automated tests, participate in architecture and code reviews, and support on-call operations. Use generative AI to improve pipeline development, monitoring, and engineering productivity.

Builds and maintains scalable data pipelines and cloud infrastructure supporting an AI biomedical research platform. Responsibilities include ingesting, harmonizing, validating, and documenting multimodal neurodegeneration datasets; managing cloud storage, compute, permissions, and costs; implementing de-identification and data governance workflows; supporting research data repositories and AI/ML teams; and maintaining data lineage, catalogs, and technical documentation. The role requires strong Python and SQL skills, software engineering discipline, cloud experience, and neurodegeneration research data expertise.

Lead architecture and hands-on engineering to design, scale, and govern a cloud-native lakehouse using Databricks and Microsoft Fabric. Build batch/streaming pipelines, ensure HIPAA-compliant PHI handling, produce analytics-ready datasets, mentor engineers, and drive platform strategy, governance, and reliability.

Design, build, and maintain ETL pipelines, databases, and data models to process healthcare claims and related data. Ensure data quality, analyze claims data for trends, and produce reports and visualizations for nontechnical stakeholders. Collaborate with policy and analytics teams to translate requirements into data solutions and improve data infrastructure and analytics capabilities.

Design and implement end-to-end data solutions on Microsoft Fabric and Azure for insurance datasets. Build ingestion pipelines (batch, streaming, API), Lakehouse architectures, and data models. Enforce governance, security, compliance, and cost/performance optimizations while guiding engineers and stakeholders on architectural best practices.

Develop and maintain large-scale, cloud-native data services and distributed pipelines for multi-tenant healthcare systems. Responsibilities include data modeling, database component development, SQL programming, performance tuning, testing, deployment, code reviews, proof-of-concepts, and full software development lifecycle ownership. Collaborate with global engineering teams, evaluate technologies, apply security and operational requirements, and use AI tools to accelerate development.

Build and scale an enterprise metrics store, dbt models, semantic-layer definitions, and analytical workflows. Develop governed business metrics, root-cause analysis, metric relationships, and reusable logic for BI tools. Partner with product, engineering, sales, underwriting, claims, and experience teams to translate business questions into actionable insights. Improve data quality, define data contracts, debug upstream and analytical issues, and help prepare metric infrastructure for API-based serving.

Leads the design, automation, optimization, and maintenance of scalable data pipelines using Airflow, dbt, Python, SQL, and Google Cloud technologies. Manages dbt models, develops reusable architecture patterns, monitors pipeline performance, supports CI/CD and testing, and ensures data security and governance. The role also provides technical leadership, mentors engineers, collaborates cross-functionally, documents implementations, and drives continuous improvement across data engineering practices.

Build and maintain scalable batch and real-time data pipelines, model data to power analytics and ML, and operate distributed data systems. Collaborate with business, engineering, and data science teams to ensure data quality, documentation, and debugging while adopting new technologies and working in an ambiguous, fast-changing environment.

Build and maintain low-latency, highly available distributed data systems and services to deliver high-quality data for product, analytics, and ML. Partner with data scientists and product teams to design scalable, observable, multi-tenant data products using batch and real-time processing frameworks. Ensure 24x7 reliability, monitoring, and operational excellence while owning end-to-end delivery.

Build scalable, privacy-focused data frameworks and ETL pipelines using big-data technologies (Spark, Presto, Flink, Kafka, Hive). Ensure appropriate handling of personal data, support privacy and legal needs, partner cross-functionally, and lead system design while mentoring teammates.

Design, build, and own large-scale batch and streaming data pipelines and core Ads data products. Collaborate with analytics, data science, and engineering teams to model, transform, govern, and deliver high-quality ad datasets for analysis, ML, and reporting while ensuring privacy compliance.

Designs and leads scalable data platforms, event-driven services, orchestration pipelines, and DataOps practices using Python, Airflow, Snowflake, Kafka, and related technologies. The role establishes engineering standards, governance, security, observability, cost efficiency, and AI-assisted development practices. It also mentors engineers, drives cross-functional initiatives, modernizes data ingestion, and ensures reliable, compliant, and well-governed systems.

Lead database engineering for a federal species-data integration program in a hybrid AWS cloud environment. Responsibilities include harmonizing schemas, documenting source-to-target mappings and lineage, enforcing metadata and data-quality standards, implementing encryption and NIST-aligned security controls, building authoritative source registries, optimizing performance, and leading production migration, validation, monitoring, and operational documentation. The role requires Databricks or equivalent lakehouse expertise, ETL programming, federal governance knowledge, and database migration leadership.

Designs secure Azure data pipelines and analytics foundations using Databricks and ADLS Gen2. Responsibilities include modernizing Oracle workloads, developing medallion data models, implementing data quality and monitoring practices, integrating curated datasets with Power BI, and establishing secure Azure Government identity, network, storage, and access patterns. The role also requires documentation, operational support, stakeholder collaboration, and performance optimization.