Top Senior Data Engineer Jobs in Tampa, FL

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Own and evolve UJET’s data platform, including MySQL CDC replication to BigQuery, dbt transformations, Terraform infrastructure, pipelines, data quality, observability, and Looker models. Build reliable, analytics-ready datasets and trusted metrics for internal and customer-facing reporting. Partner with Analytics, Finance, Product, and Engineering to deliver scalable data solutions, improve platform reliability, optimize performance and costs, and establish development standards and guardrails.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Build and operate production pipelines for legal records from PACER, court systems, opinions, PDFs, scans, XML, and HTML. Develop LLM-assisted extraction workflows, validate data quality, optimize SQL, and support RAG and semantic search products. Manage AWS S3 and RDS systems within security guardrails while collaborating with engineering teams. The role requires strong Python and SQL skills, legal data experience, document extraction expertise, and autonomous problem-solving with large, messy datasets.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.

Develop and enhance Collibra-based data governance solutions, including workflows, automations, APIs, integrations, metadata management, data cataloging, lineage, data quality, and controls. Translate governance requirements into scalable technical implementations, troubleshoot platform and integration issues, document solutions, and support Agile delivery. Collaborate with data stewards, engineering, application development, information security, product management, and business stakeholders across development and production environments.

Lead the design, development, optimization, and monitoring of scalable cloud data pipelines using AWS Glue, PySpark, and Amazon Redshift. Integrate diverse data sources, ensure data quality, deploy infrastructure with Terraform, and improve reliability, performance, and cost efficiency. Collaborate with data professionals and stakeholders, document architecture standards, ensure security compliance, mentor junior engineers, conduct code reviews, and evaluate AWS technologies. The role requires a bachelor’s degree, substantial cloud data engineering experience, and hybrid work in specified locations.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Lead the architecture and development of Vercel’s next-generation data platform, supporting batch and real-time integrations, analytics, data warehousing, and AI/ML workloads. Design scalable systems using Kafka, ClickHouse, Tinybird, and Snowflake; establish data governance and security standards; guide architectural decisions and roadmaps; collaborate with engineering, product, security, compliance, and leadership teams; write production code; and mentor engineers.

Build and maintain Databricks-based data platforms, including ingestion, transformation, storage, governance, data modeling, and serving pipelines. Establish medallion architecture standards, canonical data models, quality controls, lineage, schema evolution, and reliable batch or incremental processing. Improve pipeline observability, scalability, idempotency, and recoverability while moving curated data to systems such as ClickHouse. Collaborate across application and analytics teams to create durable, governed production datasets.

Develop and optimize enterprise Oracle SQL and PL/SQL solutions, including data models, database designs, APIs, ETL processes, batch jobs, and performance tuning. Collaborate with cross-functional teams to translate requirements into scalable implementations, support testing and CI/CD, troubleshoot production issues, and document solutions. Provide technical leadership, mentor engineers, participate in architecture and code reviews, and drive engineering best practices across data modernization initiatives.

Design, build, and operate scalable ETL/ELT pipelines and backend data services using Go. Integrate relational, NoSQL, blockchain, API, web, and social data sources; develop NLP pipelines for text extraction and classification; and support production APIs, dashboards, alerts, and intelligence systems. Collaborate with engineering, data science, and investigations teams on architecture, data quality, lineage, validation, monitoring, and client-facing products.

Leads data engineering and data science teams, setting strategy and delivering scalable pipelines, data architecture, models, analytics infrastructure, and data governance. Partners with product, analytics, and business stakeholders to prioritize initiatives, assess upstream changes, maintain data quality, and support Snowflake-based warehousing. This hands-on leadership role includes mentoring staff, contributing to technical codebases, and advancing data science and engineering solutions.

Design, build, and maintain scalable batch and streaming data pipelines using Python, SQL, Kafka, and GCP. Develop REST and GraphQL data services, CI/CD automation, observability, and data-quality frameworks. Integrate ML and NLP models into production workflows, including continuous retraining and real-time inference. Support structured and unstructured healthcare datasets, distributed systems, microservices, and reliable data infrastructure while collaborating with data science and MLOps teams.

Build and configure a government Databricks workspace, develop ingestion and integration pipelines from enterprise and legacy systems, and prepare data for reporting. Implement Unity Catalog governance, metadata management, auditing, and lineage; configure MLflow workflows; assess security controls for sensitive data; and create architecture documentation, data dictionaries, and SOPs to support long-term sustainment.

Lead the design and optimization of healthcare data pipelines integrating EHR, payer, HIE, and vendor data. Build AWS-based ETL/ELT infrastructure, reusable integration tooling, data quality monitoring, and ML enablement foundations. Ensure HIPAA-compliant handling of clinical data, including security controls, audit logging, encryption, and de-identification. Provide technical leadership, mentor engineers, influence platform architecture, and lead complex partner integrations involving FHIR, HL7v2, CCDA, and healthcare terminology standards.

Design and lead scalable, secure, highly available database infrastructure for multi-terabyte SQL Server environments. Set technical direction for data architecture, performance, real-time pipelines, security, and growth. Troubleshoot complex production issues, optimize queries and indexes, guide cross-functional projects, mentor engineers, review changes, reduce technical debt, and evaluate emerging technologies. Monitor database health using Splunk, Datadog, and Grafana while influencing engineering and Site Reliability strategy.

Designs, develops, and supports scalable ETL pipelines and large-scale data platforms using Python, PySpark, SQL Server, SSIS, and Hadoop technologies. Responsibilities include database performance tuning, data modeling, production troubleshooting, batch scheduling, data-quality controls, CI/CD, secure development, and collaboration across technical teams. The role also involves mentoring developers and evaluating AI-assisted development tools.

Leads the design, development, optimization, and security of very large SQL Server databases and data warehouses. Oversees ETL and Microsoft BI pipeline development, data modeling, performance tuning, code reviews, and access management. Collaborates with DBAs, mentors developers, applies AI-assisted optimization, assesses risks, and drives scalable, reliable data products.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Lead technical design and hands-on development of Pantheon’s data platform, extending ingestion, curation, retention services and ML/LLM infrastructure. Mentor engineers, drive architecture and engineering standards, operate in a DevOps model, partner cross-functionally, and participate in on-call support to ensure reliable, compliant data access and analytics.