Top Senior Data Engineer Jobs in Miami, FL

Leads payer healthcare data engineering and analytics initiatives, designing pipelines, data models, and interoperability frameworks for claims, membership, and provider data. Directs large technical programs, advises clients on data architecture and modernization, validates data quality and HIPAA controls, applies statistical and machine learning methods, coaches technical teams, and presents actionable recommendations to stakeholders.

Design and develop data pipelines, integrations, transformations, and infrastructure across AWS, Snowflake, Airflow, and MySQL. Troubleshoot warehouse issues, validate data quality, improve pipeline reliability, and translate business needs into data engineering solutions. Interpret technical findings for client recommendations, manage workstreams, review junior team members’ work, and provide technical guidance across managed services engagements.

Designs and evolves enterprise cloud data lake and lakehouse architectures, including Medallion architectures, ingestion frameworks, ETL/ELT pipelines, dimensional models, semantic layers, and data integrations. Establishes data quality, governance, lineage, metadata, security, CI/CD, and infrastructure-as-code standards while collaborating with stakeholders and application teams. Requires strong SQL and experience with Snowflake, Microsoft Fabric, or Databricks. Requires 10–25% domestic and international travel and four days weekly in a VSE office.

Lead enterprise data engineering for a cloud-based Data Lake/Lakehouse. Design Medallion architectures, ingestion frameworks, ELT/ETL pipelines, dimensional models, semantic layers, and data integrations. Establish data quality, governance, lineage, metadata, security, and compliance standards while optimizing storage and query performance. Collaborate with stakeholders and application teams to deliver scalable data solutions using platforms such as Snowflake, Microsoft Fabric, or Databricks.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Build and maintain production data pipelines, dbt models, Airflow workflows, and governed datasets supporting People Analytics. Integrate Workday and other HR systems into reliable Snowflake data products, implement data quality monitoring, row-level security, semantic models, and AI agents. Partner with engineers, analysts, data scientists, and business stakeholders to deliver trusted data solutions, communicate technical trade-offs, enforce governance, and mentor other engineers.

Develop and lead scalable cloud data engineering solutions for data warehouse migration, modernization, analytics, and ETL/ELT projects. Design and implement solutions using Google Cloud technologies, SQL, Python, and BigQuery. Create testing strategies and plans, collaborate with customers and development teams, translate business requirements into technical solutions, and contribute to an agile environment.

Designs, builds, and optimizes scalable data pipelines, ETL workflows, integrations, and reporting solutions. Develops SQL, Python, and Spark processes; supports Azure and Microsoft Fabric data platforms; monitors production reliability; collaborates with stakeholders; participates in code reviews and documentation; and mentors junior engineers.

Designs, develops, and maintains scalable data systems, pipelines, ETL processes, databases, and data warehouses supporting public health operations. Responsibilities include data integration, cleansing, transformation, governance, security, monitoring, optimization, and collaboration with analysts, data scientists, and stakeholders. The role also provides technical guidance, training, and mentorship while supporting sustainable data infrastructure and traveling domestically up to 10%.

Build and maintain scalable ETL/ELT pipelines, integrate data from APIs, databases, and third-party systems, and develop reliable data models for analytics and reporting. Ensure data quality, governance, security, and accessibility while supporting batch and real-time processing. Monitor pipeline performance, troubleshoot failures, and collaborate with Product, Analytics, Engineering, and Cloud teams to improve data architecture and enable data-driven decisions.

Build and operate scalable data pipelines, governed data models, semantic layers, and analytics products. Partner directly with business stakeholders to translate requirements into production-ready data and AI solutions. Develop SQL and Python applications, LLM and agentic workflows, retrieval systems, and automated testing. Ensure data quality, lineage, monitoring, reliability, cost efficiency, and compliance for PHI and genomic data. Collaborate with platform and AI teams on architecture, governance, CI/CD, and operational excellence.

Leads a small data engineering team while contributing hands-on development. Responsibilities include designing and maintaining dbt and Python ETL pipelines, managing data quality and UAT, coordinating implementation timelines, conducting code reviews, mentoring engineers, documenting systems, and supporting project delivery. The role also involves building and deploying AI agents for data quality, pipeline monitoring, and analytics workflows, with a focus on healthcare claims data and large-scale datasets.

Lead the technical direction, architecture, reliability, observability, and modernization of a cloud-based data engineering platform. Design scalable pipelines and reusable frameworks supporting analytics and AI, improve data quality, and establish testing, code review, CI/CD, and engineering standards. Drive adoption of AI-assisted development tools and build agentic workflows across planning, coding, testing, and deployment. Partner with machine learning, MLOps, Analytics Engineering, and business stakeholders in a highly autonomous senior individual contributor role.

Lead the reverse-engineering and migration of legacy ETL pipelines from SAP BusinessObjects Data Services and JD Edwards into dbt and Databricks architectures. Build automated data validation, maintain SQL Server write-back mechanisms, collaborate with client stakeholders, and lead and mentor a small engineering team. The role requires hands-on expertise in SQL, Python, Spark, orchestration, cloud platforms, and legacy ERP and ETL migrations.

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.

Build and own World Emblem’s centralized Microsoft Fabric data platform. Develop ETL/ELT pipelines from ERP, ecommerce, CRM, middleware, and internal databases; engineer SQL databases and dimensional models; implement data quality, monitoring, security, governance, and CI/CD; and enable Power BI reporting and AI initiatives through reliable, documented datasets.

Leads data architecture and engineering modernization across business domains. Designs and implements complex pipelines, integrations, data models, and orchestration using Snowflake, AWS, and Airflow. Establishes data quality, governance, security, testing, observability, CI/CD, and reliability practices. Provides architectural guidance, evaluates technical solutions, leads cross-team initiatives, mentors engineers, and improves platform performance and cost efficiency.

Build and maintain scalable Snowflake-based ELT/ETL pipelines, production data models, and observability frameworks. Translate business requirements into reliable solutions supporting analytics, BI, and ML. Establish data quality, governance, lineage, security, and compliance practices while optimizing warehouse performance, cost, and data freshness. Collaborate with Analytics, Product, and Engineering teams, contribute to architecture decisions, evaluate Snowflake tooling, and use AI-assisted development tools to accelerate delivery.

Designs, builds, and optimizes scalable data collection, storage, access, and analytics systems. Creates and automates reliable data pipelines, supports data quality and governance, ensures SLA and performance compliance, and monitors pipeline operations. The role requires collaboration with analytics, business, and customer teams, with potential work involving AI/ML pipelines, Databricks, DevOps practices, streaming data, and event-driven architectures.

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop data warehouse models, transformations, automation, quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop warehouse models, transformations, automation, data quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Build and maintain scalable batch and streaming data pipelines, analytics warehouses, transformation layers, and dimensional data models. Perform SQL-based analysis, support dashboards and reporting, monitor data quality and delivery SLAs, troubleshoot incidents, and improve operational reliability. Collaborate with engineers, analysts, data scientists, and business stakeholders while contributing to documentation, governance, observability, and automation.

Build scalable data ingestion, transformation, and loading infrastructure using Spark, SQL, AWS, and Databricks. Develop entity resolution systems, CI/CD pipelines, anomaly detection, and data quality processes. Design scalable processing systems, orchestration workflows, storage patterns, and data warehouse solutions. Collaborate remotely, document technical designs, scope projects, and solve complex data engineering and data science problems.

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.

Build scalable cloud-native data and machine learning solutions using Databricks, Spark, PySpark, and SQL. Develop batch and real-time healthcare data pipelines, enterprise data models, Lakehouse architectures, semantic layers, and AI-assisted analytics workflows. Partner directly with business and clinical stakeholders to discover requirements, prototype solutions, deliver demos, drive adoption, and provide technical leadership. Ensure HIPAA-compliant security, governance, lineage, auditability, and regulatory readiness.