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

• Contribute to the design and continuous improvement of the enterprise DLP architecture across network, endpoint, email, and cloud channels
• Deploy, configure, tune, and document DLP solutions across network egress points (web proxy, email gateway, API channels) and endpoint agents at enterprise scale
• Support the development of DLP policies aligned with data classification frameworks and labeling taxonomies across all data channels
• Conduct regular policy effectiveness reviews and false-positive tuning sessions
• Engineer end-to-end event monitoring pipelines, integrating DLP alert telemetry into SIEM platforms for correlation, enrichment, and dashboarding
• Develop automation workflows and playbooks to accelerate DLP incident triage, notification, and response.

Senior Data Loss Prevention (DLP) Architect, Engineer

• Lead the overall architecture, planning, and execution of SAP BRIM data migration initiatives
• Define migration strategy, frameworks, standards, governance, and best practices
• Design end-to-end migration solutions for various data types
• Assess legacy system data structures and map them to SAP BRIM models
• Perform data profiling, cleansing, transformation, validation, and reconciliation activities
• Provide leadership to migration developers and technical teams

SAP BRIM Data Migration Architect

• Lead the design, development, and continuous modernization of all CMS sales training curriculum across onboarding, advanced development, and leadership programs
• Architect and deploy scalable digital learning ecosystems, including LMS platforms, virtual delivery models, and AI-driven learning tools
• Integrate artificial intelligence to personalize learning pathways, enhance coaching effectiveness, and drive predictive performance insights
• Develop data platforms and dashboards that connect training initiatives to field execution, adoption, and business performance
• Establish standardized curriculum frameworks grounded in adult learning principles, behavioral change theory, and competency-based development
• Partner with field leadership to identify capability gaps and develop targeted programs to elevate clinical expertise and business acumen
• Lead transformation of existing training content into modern, interactive, and digitally enabled learning experiences
• Drive cross-functional collaboration with Marketing, Sales Operations, and Medical Affairs to ensure alignment with product strategy, clinical evidence, and market dynamics
• Continuously evaluate emerging technologies, AI capabilities, and digital learning trends to keep ZOLL at the forefront of medical device training innovation
• Support and enhance national and regional sales meeting content through innovative curriculum design and delivery strategies
• Elevate clinical acumen across the organization by developing advanced training focused on cardiac physiology, therapy application, and patient pathways
• Strengthen business acumen by building programs focused on territory strategy, account prioritization, and value-based selling
• Enable managers to effectively coach to both clinical and commercial competencies
• Create differentiated learning journeys for new, developing, and tenured representatives to continuously advance performance.

Curriculum Development Manager, Data Platforms

• 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

• The Principal Translational Knowledge Architect & Graph Lead will be responsible for designing and implementing the semantic and knowledge architecture that enables AI-driven reasoning across the drug discovery and development lifecycle.
• This role will serve as the scientific and technical lead for ontology development, knowledge graph design, semantic interoperability, and AI-ready knowledge representation.
• Working at the intersection of translational science, patient safety, biomedical informatics, and artificial intelligence, this individual will help establish the semantic foundation required to connect discovery biology, preclinical safety, clinical development, real-world evidence, and post-marketing safety into a unified reasoning framework.
• The successful candidate will partner closely with scientists, safety experts, data scientists, AI engineers, and platform teams to create knowledge assets that support GraphRAG, agentic AI, scientific reasoning, and next-generation translational intelligence capabilities.

Principal Scientist, Data Science – Translational Knowledge Engineering

• 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

• 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

• 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

• Design, develop and maintain scalable and efficient data pipelines and ETL processes
• Collaborate with analysts and stakeholders to gather requirements and implement data solutions that meet business needs
• Proposes & designs data solutions to address complex business needs
• Prepare technical and procedural documentation required
• Participate in code reviews & testing
• Develop and manage data integrations between various systems, ensuring seamless data flow and consistency
• Optimize database and query performance to support analytical workloads
• Monitor and troubleshoot data workflows to ensure data quality and integrity

• Lead the design, planning, configuration, and delivery of advanced automation and control system projects across power, industrial, and mission‑critical environments.
• Serve a technical resource for control system architecture, integration strategy, troubleshooting, and technical decision‑making.
• Manage project scope, schedules, budgets, and technical quality while partnering closely with cross‑functional engineering, project management, and customer teams.
• Apply advanced knowledge of control systems, microgrid technologies, and specialized industrial applications to develop effective solutions.
• Oversee customer‑facing activities including design reviews, validation, factory and site acceptance testing, commissioning, and final system handover.
• Collaborate with global engineering teams to identify innovative solutions, share best practices, and maintain consistent project execution standards.

Senior Project Engineer – Microgrids, Data Centers

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes.
• Build and optimize large-scale data processing solutions using Python, PySpark, and Java.
• Develop data ingestion frameworks for structured and unstructured data sources.
• Integrate data from various systems through REST APIs, streaming platforms, and batch processing.
• Work with large datasets in distributed computing environments.
• Develop and support real-time and batch data processing solutions using Kafka and Hadoop ecosystem technologies.
• Implement scalable streaming data pipelines and event-driven architectures.
• Monitor and optimize data workflows for performance and reliability.
• Write complex SQL queries for data extraction, transformation, and analysis.
• Design and optimize database schemas and data models.
• Ensure data quality, consistency, and governance standards are maintained.
• Participate in Agile ceremonies including sprint planning, daily stand-ups, backlog grooming, and retrospectives.
• Use Jira for project tracking, issue management, and sprint execution.
• Collaborate with Data Architects, Data Scientists, Business Analysts, and Application Development teams.
• Work independently as well as within a collaborative team environment.
• Troubleshoot and resolve data-related issues across environments.
• Perform root cause analysis and implement long-term solutions.
• Support production deployments and ongoing maintenance activities.

• Define the target architecture for the enterprise data context self-service platform.
• Establish architectural standards and patterns for: context publishing discovery access control versioning validation lineage and provenance observability and monitoring
• Partner with product, engineering, service design, security, and governance teams to ensure the platform is technically sound and aligned to enterprise needs.
• Evaluate and recommend technologies and integration patterns including: ontology and semantic modeling approaches OWL/RDF-based systems knowledge graphs semantic layers MCP, A2A, and Timbr-like technologies where applicable
• Design architecture that supports both self-service user workflows and AI/agentic consumption patterns.
• Ensure context can move from development to production in a governed, scalable, and supportable way.
• Define non-functional requirements including performance, resilience, security, compliance, and operational maintainability.
• Review solution designs and implementation plans to ensure adherence to architectural standards.
• Create reference architectures, patterns, and decision records to guide implementation teams.
• Collaborate with platform teams to support long-term scalability, reuse, and interoperability across domains.
• Help shape how the platform supports agentic AI workflows, including structured prompt context, tool access, and reliable data grounding.

Lead Architect, Data Context Layer

• Design, build, optimize, and operate production-grade batch and streaming data pipelines on Azure and Databricks, with a primary focus on real-time IoT and telemetry use cases within a Medallion architecture.
• Develop ETL/ELT workflows to ingest, transform, validate, and serve large volumes of structured, semi-structured, unstructured, and streaming data.
• Build and maintain reliable data products, data services, APIs, and microservices that support operational applications, analytics, software engineering, and ML/AI teams.
• Use Python, PySpark, Spark SQL, SQL, Delta Lake, Databricks Workflows, CI/CD, and related tools to build maintainable, testable, and observable data systems.
• Troubleshoot complex production pipeline issues across Databricks, Azure, streaming systems, APIs, and source systems, including root cause analysis, corrective action, and prevention planning.
• Move quickly from rough business need to prototype, pilot, and production-ready data capability while maintaining appropriate engineering discipline.
• Lead the design and delivery of real-time streaming ingestion and processing patterns for connected medical device telemetry, event data, and operational data feeds.
• Implement streaming solutions using Azure Event Hubs, Azure Stream Analytics, Databricks, Delta Lake, and related Azure integration patterns.
• Define reliability, latency, quality, observability, and supportability expectations for production streaming systems.
• Manage, mentor, and develop data engineers, providing clear expectations, technical guidance, prioritization support, feedback, and accountability.

Principal 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

• Build Architect and build production-grade data pipelines (batch and streaming) for the highest-priority or highest-ambiguity initiatives.
• Write code, design model architectures, and personally develop and deploy machine learning models and generative AI applications.
• Stay hands-on in production: debug critical-path issues, review production-critical code and model logic, and remain a credible technical authority by virtue of doing the work, not just overseeing it.
• Define and execute the AI/ML roadmap for the line of business, identifying high-value use cases across the full data landscape.
• Provide overarching oversight and governance of enterprise data architecture standards, design patterns, and data modeling principles.
• Review and approve solution designs across engineering, AI, and vendor deliverables for alignment with data architecture standards.

Director, Data Engineering – AI

• Develop technical enablement content, including presentations, solution guides, white papers, workshops, labs, and training materials.
• Design, build, and maintain product demonstrations that showcase customer challenges, technical capabilities, and business outcomes.
• Support field sales, systems engineering, and channel teams by providing technical expertise and creating customer-facing content.
• Deliver presentations at customer meetings, internal enablement sessions, partner events, and industry conferences.
• Collaborate closely with Product Management, Engineering, Marketing, and regional field organizations to support product launches and strategic initiatives.
• Stay current on emerging technologies and industry trends in data center networking, automation, AI, and cloud infrastructure.
• Translate complex technical concepts into clear, compelling messaging for technical and executive audiences.

Senior Technical Marketing Engineer – Apstra Data Center

• Design, develop, and maintain scalable ETL/ELT pipelines using Python and SQL.
• Build and optimize data ingestion and transformation frameworks for large datasets.
• Develop robust solutions for data processing, validation, and quality assurance.
• Implement CI/CD pipelines and DevOps practices for data engineering workflows.
• Automate data pipelines, deployment, and monitoring processes.
• Work with cross-functional teams to understand business requirements and deliver data solutions.
• Troubleshoot and optimize performance of data pipelines and systems.

• Design, build, and optimize robust ETLs using PySpark and DBT to process large-scale customer datasets.
• Define the technical vision for DC data architecture, mentor engineers, and manage external contractors to ensure the team delivers high-quality, practical solutions for current and future needs.
• Partner with product, engineering, and applied science teams to scope work and deliver data solutions that address real-world challenges in customer data quality and product feature requirements.

Staff Software Engineer – DC Tech Lead

• Design, build, and maintain robust data pipelines and data lake architecture for both batch and real-time streaming use cases, including high-volume, low-latency data processing
• Improve observability, alerting, and SLOs across data systems so pipelines are easier to monitor and issues are caught early
• Optimize ETL/ELT workflows for performance, scalability, and fault tolerance
• Develop dbt workflows to onboard Evaluation Partners and create end-of-day reporting for partner analysis
• Build and support event-driven architectures and scalable platform components
• Contribute to the orchestration and automation of workflows
• Integrate complex financial APIs and third-party data sources into internal systems
• Collaborate with analytics, product, and ML engineers to develop and deploy reliable data products
• Work on feature pipelines and model-ready data to support ML engineers
• Promote high standards in code quality, testing, and platform reliability
• Participate in Agile ceremonies and foster a collaborative, growth-oriented team culture

Data Engineer jobs in Pennsylvania

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Senior Data Engineer

Senior Data Loss Prevention (DLP) Architect, Engineer

SAP BRIM Data Migration Architect

Curriculum Development Manager, Data Platforms

Senior Data Engineer

Principal Scientist, Data Science – Translational Knowledge Engineering

Senior AI and Data Architect

Senior Software Engineer, Data

Senior Software Engineer, ERP, Data & Domain

Senior Data Engineer

Senior Project Engineer – Microgrids, Data Centers

Data Engineer

Lead Architect, Data Context Layer

Principal Data Engineer

Data Engineer II

Director, Data Engineering – AI

Senior Technical Marketing Engineer – Apstra Data Center

Data Engineer

Staff Software Engineer – DC Tech Lead

Senior Data Engineer