Data Engineer

• Provide technical leadership to data engineers, setting standards for solution design, coding practices, data governance, and quality.
• Define and evolve the enterprise data architecture leveraging Snowflake, Data Vault 2.0, and modern event driven and ELT frameworks.
• Lead architecture reviews, establish engineering best practices, and guide platform modernization efforts.
• Mentor engineering team members, facilitate code reviews, and promote continuous learning and innovation.
• Architect and oversee the delivery of scalable data pipelines, ingestion frameworks, and transformation processes using Snowflake, Python, Spark, Informatica (PowerCenter/IDMC), AWS Glue, and cloud-native tooling.
• Design and maintain enterprise data models, including Data Vault 2.0 (Hubs, Links, Satellites, PIT, Bridge structures) and dimensional models.
• Develop and optimize Snowflake platform capabilities, including: Snowpipe, Streams, Tasks, File Formats, External Stages Dynamic Tables and ELT pipeline automation Warehouse sizing, performance tuning, and cost optimization.
• Implement scalable batch, micro-batch, and real-time ingestion solutions following best practices.
• Translate complex business requirements into technical design specifications and actionable development plans.
• Partner with product owners, business stakeholders, architects, and analytics teams to deliver high-impact data solutions.
• Manage technical execution across multiple initiatives, ensuring alignment with enterprise priorities, data strategies, and delivery timelines.
• Oversee documentation, deployment readiness, testing processes, and quality assurance for production releases.
• Ensure operational reliability, data accuracy, and performance of enterprise data warehouse and analytical environments.
• Lead root-cause analysis for production issues and drive implementation of long-term preventative solutions. Implement robust monitoring frameworks using tools such as Snowflake Resource Monitors, CloudWatch, Datadog, or equivalent observability platforms.
• Conduct performance optimization on Snowflake workloads, SQL queries, pipelines, and integrations.

Senior Data Engineer

• Design, implement, and oversee the enterprise data governance strategy
• Work in close partnership with the Ontology/Semantic Librarian and Data Engineering teams
• Lead the development and enforcement of data governance policies, standards, and best practices
• Collaborate with Data Engineering teams to implement governance in federated querying, GraphQL interfaces, vector databases, and RAG pipelines
• Define and maintain enterprise data stewardship processes
• Establish governance frameworks for data security, privacy, and compliance
• Design and implement data quality, metadata, and lineage strategies
• Contribute to the internal Data Marketplace by defining governance requirements for semantic data products
• Drive data governance training, change management, and adoption across business, engineering, and AI teams
• Monitor and report on governance metrics, risks, and compliance posture for the Data Unification program

Lead Data Engineer – Governance

• Lead the design, development, and governance of enterprise ontologies, semantic layers, and knowledge graphs.
• Combine deep semantic modeling expertise with hands-on implementation of graph technologies, vector databases, and federated data architectures.
• Design and build enterprise ontologies and semantic models that align business objectives with technical implementation.
• Lead the creation and maintenance of semantic layer graphs, knowledge graphs, and entity graphs.
• Develop and implement strategies for using vector databases and graph databases to enable powerful LLM-augmented search and reasoning.
• Partner with data engineering, AI, and business teams to translate business goals into ontological models.
• Contribute to the development of a unified data access layer that supports querying across diverse data sources.
• Implement and enforce enterprise security models through the ontology and semantic layer.
• Collaborate on AI integration initiatives, including building ontology-driven agents.
• Establish ontology governance processes and contribute to the internal Data Marketplace.

Lead Data and Ontology Engineer

• Design, build, and maintain scalable data pipelines across internal databases, vendor systems, and our data warehouse
• Support both batch and event-driven or CDC-based data workflows
• Improve pipeline reliability, observability, and performance
• Troubleshoot issues in production and proactively solve data quality or system problems
• Build and maintain data warehouse schemas and transformation layers to support analytics and operational use cases
• Communicate clearly about implementation choices, tradeoffs, and technical constraints
• Identify opportunities to improve data workflows through better tooling, automation, and engineering practices
• Help shape scalable patterns for how data is ingested, transformed, stored, and accessed

• Serve as the go-to subject matter expert on Salesforce Agentforce for internal teams and enterprise customers.
• Advise definitively on optimal use cases, implementation patterns.
• Act as an escalation point for critical or complex Agentforce project challenges.
• Lead the technical design and hands-on development of complex Agentforce solutions.
• Partner with customers and account teams to assess business challenges, identify high-impact Agentforce use cases.
• Lead and contribute to mentorship and enablement programs.
• Maintain a high degree of expertise on the rapidly evolving Agentforce product roadmap.
• Provide critical “Voice of the Customer” feedback to Product and Engineering organizations.
• Actively partner with GTM counterparts during pre-sales activities.

Senior AI and Data Architect

• You will own and evolve our data pipelines on GCP — building new ones, hardening existing ones, improving data quality, and making clean, trustworthy data available across the organisation.
• Designing ELT/ETL processes on BigQuery and ClickHouse, building real-time pipelines on Pub/Sub and Kafka with Dataflow (and where it fits, Flink/Spark).
• Orchestrating workflows with Airflow, and ensuring data is properly cleaned, modelled, and served for analytics, ML training, and online inference.
• Partnering with ML engineers on feature pipelines, monitoring data drift, and keeping models well-fed and retrained as needed.
• Consuming and building REST APIs, integrate with third-party SaaS sources, and treating infrastructure as code.
• Collaborating closely with Infrastructure, Software Engineering, Product, and ML/AI engineers.
• Helping drive the migration off Snowflake onto GCP-native stack — and retire shadow pipelines along the way.
• Owning the orchestration layer in Airflow, including SLAs, retries, and data quality gates.
• Modeling data for analytics and for ML — including feature pipelines that serve both training and low-latency online inference.
• Capturing requirements from stakeholders and translating them into pragmatic, well-scoped data products.
• Continuously improving data quality, reliability, observability, and cost efficiency.
• Identifying new data sources worth acquiring and integrating them cleanly.

• Size, architect, and design data protection, disaster recovery, and scale-out storage solutions
• Bring new products and processes to life to meet customer demand, improve service SLAs, and minimize gaps
• Serve as the escalation point for L1, L2, and L3 support engineers
• Work directly with customers to solve challenging design and support issues
• Automate the collection of backup system health and streamline tasks
• Monitor backup systems and performance, identifying and troubleshooting issues
• Configure access using a least-privileged approach, audit, and ensure adherence to policies
• Communicate upgrade and rollback plans, and execute upgrades
• Build lasting trust with clients

Data Protection Engineer

• The Data Engineer, AI Enablement develops and maintains data pipelines and datasets that support analytical reporting and artificial intelligence solutions.
• Using a variety of data engineering methods, this role works with structured, semi-structured, and unstructured data to prepare datasets for analytics, artificial intelligence, agents, and modeling use cases.
• Support the construction and ongoing maintenance of data structures used for data-driven and AI-enabled analyses.
• Assess and report on data quality and contribute to efficiency improvements by aligning data and AI systems with defined business and analytical goals.
• Enable analysts, AI developers, and business stakeholders to access, consume, and operationalize valid and reliable data across internal and external use cases.

Data Engineer II, AI Enablement

• Building and leading a team to deliver technology products and services that meet business outcomes.
• Developing a technology strategy, ensuring technology solutions comply with applicable standards.
• Promoting design, engineering, and organizational practices.
• Advocating and advancing modern, Agile solution delivery practices.
• Coaching, mentoring, providing feedback and hands-on career development.
• Identifying emerging talent and fostering leadership skills.
• Managing stakeholders and relationships with business and technology partners.
• Contributing to the technology strategy for their technical domain.
• Creating an inclusive and healthy working environment.

Senior Technical Manager – Enterprise Data Platform

• Lead a team of 2-3 engineers
• Working with fellow Nava engineers to design, review, and build well-crafted software
• Working in an agile manner to efficiently ship new features that meet user needs
• Design and develop scalable data ingestion and processing pipelines
• Implement large-scale data ecosystems within cloud-based platforms that include data management and data governance of structured and unstructured data
• Implement data validation and quality checks to ensure accuracy and consistency
• Work with cross-functional project teams to gather business requirements and translate to detailed technical specifications
• Work with Government partners to assist and develop data engineering applications and pipelines that will enable data services and processing capabilities
• Participate in software design and code reviews
• Maintain security and privacy standards in all aspects of the data pipeline
• Taking part in hiring activities (e.g., submitting referrals, conducting interviews, and attending interview debriefs), as needed

Senior Software Engineer, Data

• Ensure data pipelines comply with security policies and regulatory requirements (e.g., HIPAA, NIST, SOC 2)
• Develop testing plans to ensure quality of implementation, including deployment validation, data quality checks, reconciliation, and repeatable testing of data pipeline changes.
• Determine and perform complex configuration changes to meet business and information security requirements, including Microsoft Fabric workspaces, data pipelines, connections, service principals, secrets, and environment-specific configuration.
• Provide trouble resolution on complex problems and lead implementations for data pipelines, Microsoft Fabric, Azure DevOps, REST API, and source-system integration issues.
• Provide technical support to business and technology associates in risk assessments and implementation of appropriate information security procedures, standards and technologies.
• Represent major upgrades and reconfigurations in change control; design and analyze vendor and platform services meeting business and information security requirements, including governed promotion across development, test, and production environments.
• Provide system, data platform, and security architecture support for information security technologies and data pipeline capabilities.
• Represent infrastructure security support in significant projects and perform the most complex operations and administration tasks.
• Respond to level 3 & 4 change and problem requests without supervision; Lead level 1 & 2 incident recoveries and root cause analysis.
• Coordinate and prepare the reporting of data security events and incidents.
• Perform capacity analysis; recommend and implement capacity increases for data platform performance, scalability, and reliability.
• Serve as the technical escalation for results of preventative maintenance routines; Supervise preventative maintenance; Leads lights on initiatives to consolidate equipment and/or implement business relocations.

Data Engineer – Secure Data Pipeline

• Design and implement the canonical data model for Vultr’s control plane ERP transformation: product catalog, chart of accounts, financial transactions, cost centers
• Build and maintain the internal APIs and event streams that Finance, Analytics, and downstream systems consume
• Refactor existing administer code toward clean domain boundaries (no more conflated orders/ledger/report tables)
• Implement data upgrade/migration paths from legacy schemas without breaking production
• Write the integration layer between administer and Vultr's existing billing, provisioning, and reporting systems
• Establish data validation and integrity checks across the financial data layer
• Collaborate with the Product Manager to translate finance domain requirements into durable schema designs
• Document architectural decisions and data model contracts for downstream consumers

Senior Software Engineer, ERP, Data & Domain

• End-to-End Pipeline Engineering: Design, build, and deploy scalable ETL/ELT pipelines from diverse source systems into our Snowflake Data Cloud.
• Cloud Infrastructure: Manage and optimize data flows within an AWS environment (S3, Lambda, IAM), ensuring high availability, security, and cost-efficiency.
• High-Scale Processing: Leverage Databricks and Python (PySpark) to handle complex transformations and high-volume data processing.
• Implement the Semantic Layer: Collaborate with the team to define, implement and scale our Semantic Layer (via dbt Semantic Layer, MetricFlow, or similar) to standardize business logic, metrics, and dimensions for all downstream consumers.
• Model for Truth: Use dbt to build modular, version-controlled, and tested data models that serve as the definitive foundation for business intelligence.
• Data Governance & Quality: Implement automated testing and monitoring to ensure the integrity and reliability of finished data marts.

• Be a strong technical person on our team who helps drive innovation
• Be responsible for producing and maintaining critical data deliverables / pipelines
• Be technically talented, able to contribute quality code
• Be responsible for the quality, scope, and timeliness of all your deliverables

Principal Data Engineer

• **What You’ll Do **
• As a Senior Databricks Data Engineer at Clarkston you will:
• - Support our solution architects, software developers, data analysts and data scientists on data and analytics initiatives
• - Serve in a lead capacity on client initiatives
• - Ensure optimal data delivery architecture is consistent throughout ongoing projects
• - Be self-directed and comfortable supporting the data needs of multiple teams, systems and products
• - Be excited by the prospect of building our company’s data engineering capabilities to support our next generation of products and data initiatives
• - Lead, grow, and mentor a data engineering team in developing, maintaining, and monitoring data solutions for clients
• - Assist in defining client solutions and support managing their delivery

Senior Data Engineer – Databricks

• Build and maintain the data models and transformations that power reporting in the Skimmer product, leveraging tools like Fivetran transformations, dbt, and Sigma Computing materializations
• Build and maintain customer-facing data models and reports in Sigma Computing, embedded within the Skimmer application
• Manage and extend ingestion using Fivetran across a growing set of sources
• Develop and maintain data transformations in Python and SQL against our Snowflake data warehouse
• Partner with product and engineering to turn customer needs into reliable, performant reporting experiences
• Collaborate with application engineers to understand source systems and ensure clean, reliable data capture
• Monitor data quality, reliability, and report performance, and troubleshoot issues as they arise
• Document data models, definitions, and architecture to support a growing team

• Own the building, maintenance, and optimization of pipelines to ingest data from both operational databases and third-party tools into a data lake/warehouse.
• Architect highly efficient ingestion patterns that handle evolving data schemas and high-volume, multi-source data streams seamlessly.
• Optimize pipeline performance to ensure maximum uptime, high throughput, and cost-effective compute usage.
• Use dbt to transform raw data within the data warehouse into structured, production-ready schemas.
• Write templated SQL and Jinja code to enforce macro-driven, modular, and DRY (Don't Repeat Yourself) development practices.
• Perform rigorous data quality checks by implementing native dbt tests.
• Set up monitoring and alerting frameworks around both ingestion routines and dbt builds.
• Track pipeline health metrics to measure and report on overall data freshness and platform reliability.
• Build data applications that interact with the data warehouse to empower decentralized self-service analytics.
• Build internal tooling and libraries to facilitate analytics and ML work.

Staff Data Engineer

• Designs and develops moderately complex queries, objects, ETL packages, reporting structures, and other objects necessary to support detailed analysis and self-service reporting needs.
• Collaborates with analytics and business teams to create and improve data models that feed BI tools, increasing data accessibility, and fostering data-driven decision making across the organization.
• Takes ownership of the health, reliability, and security of the data platform by designing and implementing processes and solutions that proactively monitor data delivery across the full pipeline — ensuring data is accurate, complete, consistent, and timely from source ingestion through gold layer curation.
• Investigates, diagnoses, and resolves complex data pipeline failures across all system layers; uses findings to drive pipeline improvements and communicates status, root cause, and resolution clearly to the team throughout the full lifecycle of an incident.
• Assists with the development and delivery of data solutions, including data pipelines, API integration, and other structures and processes to support the data platform
• Identifies, designs, and implements internal process improvements, including the automation of manual processes, optimization of data delivery, and refactoring of code for improved performance
• Builds pipelines to enable the optimal extraction, transformation, and loading of data from a wide variety of data sources
• Provides strategic assistance to BI leadership and the BI team by making recommendations in the areas of data management and best practices
• Collaborates with developers, database administrators, data architects, and others on reporting and data management projects as needed
• Takes on other duties needed to help drive our Purpose, fulfill our Brand Principles, and abide by our Organization’s Values

Data Engineer II

• Collaborate with teams of IT/BI and non-IT personnel to create, manage, maintain and develop enterprise data framework and pipelines.
• Provide hands-on leadership and feedback on projects and production systems.
• Provide training, guidance, and thought leadership to the Data Engineering team.
• Take a leadership role in large-scale corporate projects such as integrations and major customer impacting system upgrades.
• Expand and optimize data pipeline framework across multiple applications and systems.
• Collaborate with the BI team and business analysts to review requirements and translate to ETL design and implementation.
• Understand ETL technical approaches and designs.
• Provide support for existing data pipeline frameworks.
• Diagnose and troubleshoot problems with existing data structures, ETL processes and logic.
• Take a leadership role in setting organizational data management and architecture strategy.
• Manage projects and workload according to FMB standard technology.

• Architect and build scalable, secure, and observable data infrastructure to power LLM-based agents, multi-agent systems, and tool-using AI workflows.
• Design and operate robust batch and real-time data pipelines supporting embeddings, RAG systems, and agent memory frameworks.
• Develop and manage vector database solutions to enable low-latency retrieval and contextual intelligence for AI applications.
• Build data frameworks for training, evaluation, benchmarking, and continuous improvement of agentic AI systems.
• Implement strong data governance, quality controls, lineage tracking, and PII/security compliance across AI data platforms.
• Collaborate with AI/ML, platform, and DevOps teams to productionize experimental AI prototypes into enterprise-grade solutions.
• Optimize data systems for performance, scalability, reliability, and cost efficiency across cloud environments (AWS, Azure, or GCP).

Data Engineer jobs in Florida

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Senior Data Engineer

Lead Data Engineer – Governance

Lead Data and Ontology Engineer

Senior Data Engineer

Senior AI and Data Architect

Senior Data Engineer

Data Protection Engineer

Data Engineer II, AI Enablement

Senior Technical Manager – Enterprise Data Platform

Senior Software Engineer, Data

Data Engineer – Secure Data Pipeline

Senior Software Engineer, ERP, Data & Domain

Senior Data Engineer

Principal Data Engineer

Senior Data Engineer – Databricks

Data Engineer

Staff Data Engineer

Data Engineer II

Senior Data Engineer

Senior Data Engineer – Agentic AI Engineering