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

• Define and drive the data science and AI roadmap, aligning model development priorities with business objectives and product strategy.
• Lead end-to-end delivery of ML and AI solutions — from problem framing, data discovery, and model design through validation, deployment, and performance monitoring.
• Translate ambiguous business problems into well-scoped data science workstreams, identifying quick wins alongside longer-term strategic initiatives.
• Champion best practices in model development, including versioning, documentation, validation, and observability.
• Design and implement NLP pipelines for use cases such as entity extraction, semantic mapping, classification, and retrieval-augmented generation (RAG).
• Build and maintain forecasting and predictive models to support operational and strategic decision-making.
• Apply statistical and machine learning methods to identify root causes of process inefficiencies and data quality issues.
• Develop reusable data pipelines, crosswalk tables, and transformation workflows that support scalable, cross-functional data products.
• Conduct current-state assessments of data architecture, sources, and quality; define future-state data models and governance standards.
• Develop and maintain KPI reporting frameworks and dashboards that enable performance monitoring and data-driven decision-making.
• Apply process optimization methodologies (e.g., Lean Six Sigma) to identify bottlenecks, reduce cycle time, and improve data accuracy.
• Ensure analytical outputs are accurate, auditable, and aligned with regulatory and compliance requirements (e.g., HIPAA, GDPR).
• Partner closely with Product, Engineering, and business stakeholders to clarify requirements, validate feasibility, and define measurable success criteria.
• Communicate complex analytical findings and model outputs clearly to both technical and non-technical audiences, including executive stakeholders.
• Define value-realization strategies for data and AI investments, ensuring ROI is tracked through improved search, reporting, and operational insight.
• Mentor data analysts and junior data scientists through pairing, design reviews, and structured technical guidance.
• Lead knowledge transfer of owned models, pipelines, and analytical frameworks to ensure team resilience and continuity.
• Drive a culture of continuous learning, analytical rigor, and responsible AI within the data science function.

Principal Data Engineer

• Engage in cross-functional collaboration within an Agile environment (Scrum/Kanban/SAFe)
• Participate in design sessions with peers and complete code reviews
• Utilize developer tooling across the software development life cycle to perform advanced-level application design, implementation, and maintenance activities
• Diagnose and correct application and infrastructure issues
• Mentor and support engineers at all junior levels
• Share best practices and improve processes within and across teams
• Occasional on-call for production support 24x7

Senior Engineer – Data

• 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 operate scalable batch and real-time data pipelines in Databricks and related tooling to support audience, personalization, and measurement use cases.
• Engineer event-driven streaming (e.g., Kafka) for loyalty, pump, and transaction events to enable near-real-time personalization.
• Build and maintain the Customer 360 data model, feature/attribute pipelines, and promotion/offer data feeds to decisioning systems.
• Implement data-quality validation, monitoring, and change management so segments, flags, and attributes are accurate and trustworthy.
• Deliver governed customer attributes and audiences to activation platforms (e.g., Hightouch, Salesforce Marketing Cloud) with clear ownership and documentation.
• Partner with data science to productionize model features and with architecture to align on data contracts and identity resolution.
• Support privacy and compliance requirements across all data flows.

• Design and manage Feed the Children’s modern cloud data infrastructure, with ownership of Microsoft Fabric and Azure.
• Lead the stand up and evolution of Feed the Children’s Fabric platform and serve as its primary Product Owner.
• Collaborate closely with ERP and CRM teams, and coordinate with Data Governance leadership.
• Ensure seamless integration of data and data flows from various sources, including ERP systems, CRM systems, DHIS2, external datasets and APIs, and other business applications.
• Implement processes and tools to ensure high data quality and consistency across the organization.

Data Architect

• 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

• Design, develop, and maintain scalable ELT/ETL pipelines using Databricks, Apache Spark, SQL, and Python
• Build and maintain reliable, high-quality datasets that support enterprise analytics, operational reporting, and AI use cases
• Develop data transformations using Delta Lake and Lakehouse architecture best practices
• Integrate data from enterprise applications, APIs, databases, files, and other internal and external sources
• Collaborate with business stakeholders, analysts, and product teams to understand data requirements and deliver scalable solutions
• Monitor, troubleshoot, and optimize production data pipelines for reliability, scalability, and performance
• Participate in migrating and modernizing legacy data processes to cloud-native architectures
• Improve data quality through validation, testing, monitoring, and automation
• Contribute to engineering best practices through code reviews, documentation, and knowledge sharing
• Support continuous improvement of the enterprise data platform by identifying opportunities for automation, optimization, and simplification

Data Engineer II

• Lead the design, development, and support of party reference data applications, APIs, and data services.
• Partner with business stakeholders, architects, and engineering teams to deliver reference data solutions supporting client, counterparty, issuer, and legal entity domains.
• Develop and maintain cloud-native data pipelines utilizing AWS services such as Glue, Lambda, EventBridge, S3, RDS, and Kafka/MSK.
• Support the modernization of legacy reference data platforms through migration to scalable cloud-based solutions.
• Integrate third-party reference data sources including Dun & Bradstreet, S&P Global, and other data providers to support enrichment, validation, and compliance requirements.
• Develop and support APIs and data services consumed by CRM, compliance, reporting, operations, and trading platforms.
• Implement data quality controls, validation rules, matching and deduplication processes, survivorship logic, and lineage tracking capabilities.
• Collaborate with data governance and architecture teams to support reference data standards, metadata management, and best practices.
• Participate in Agile planning, backlog refinement, estimation, sprint execution, and delivery activities using Jira.
• Provide technical leadership and mentorship to junior engineers and project team members.

Lead Data Engineer

• As an Operations Engineer - Data Center (SysOps), you will be part of a diverse team that is responsible for supporting mission-critical hardware and infrastructure as well as maintaining the day-to-day operations of our data centers.
• Match Group hosts several sites like Match, Plenty Of Fish, BLK and many others from our physical data centers.
• Through continued innovations, we are able to deliver a truly amazing experience to millions of singles that use our platform.
• Your role will require onsite presence in our data centers in Dallas, TX and surrounding areas, but also accommodates a hybrid schedule.
• Travel is also required while supporting multiple locations 10% of the time.

Operations Engineer – Data Center, SysOps

• 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

• Lead the design, development, and support of party reference data applications and services.
• Partner with architects and business stakeholders to implement data solutions supporting client, counterparty, issuer, and legal entity data domains.
• Build and maintain cloud-native data pipelines utilizing AWS services including Glue, Lambda, EventBridge, S3, RDS, and Kafka/MSK.
• Support modernization initiatives by migrating legacy reference data platforms to scalable cloud-based architectures.
• Integrate and manage third-party reference data providers such as Dun & Bradstreet, S&P Global, and other enrichment sources.
• Develop and maintain APIs and data services supporting compliance, CRM, operations, reporting, and trading systems.
• Implement data quality controls, validation frameworks, deduplication processes, and lineage tracking capabilities.
• Collaborate with data governance and architecture teams to support reference data standards and best practices.
• Participate in Agile ceremonies, backlog refinement, estimation, and technical delivery planning using Jira.
• Mentor junior engineers and provide technical guidance across project teams.
• Support platform monitoring, resiliency, performance tuning, observability, and operational excellence initiatives.

• 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

• Design and build scalable data ingestion pipelines from structured and unstructured data sources into Snowflake.
• Develop and maintain ELT/ETL processes to transform, cleanse, and integrate enterprise data.
• Design reusable dimensional, semantic, and business-ready data models that support analytics and AI use cases.
• Build and maintain consumable data products including curated datasets, data marts, semantic layers, APIs, and AI-ready data assets.
• Design and implement enterprise data architectures that support scalability, interoperability, and future AI adoption.
• Design and curate AI-ready datasets that support machine learning, generative AI, intelligent agents, and advanced analytics.
• Implement metadata, lineage, and semantic modeling capabilities that improve data discoverability and AI readiness.
• Collaborate with AI and analytics teams to establish patterns for retrieval, search, knowledge management, and AI-enabled business solutions.
• Evaluate and adopt emerging Snowflake capabilities, including Cortex AI, semantic models, vectorized data structures, and AI-related platform services.
• Apply Data Product Management principles by establishing ownership, quality standards, service levels, and lifecycle management processes.
• Implement data quality monitoring, automated validation, observability, and governance frameworks.
• Ensure compliance with enterprise security, privacy, regulatory, and data governance requirements.
• Establish and maintain metadata standards, data lineage, business definitions, and cataloging practices.
• Optimize Snowflake performance through query tuning, workload management, storage optimization, and architectural improvements.
• Ensure high availability, reliability, and scalability across data platforms and pipelines.
• Implementing proactive monitoring, alerting, and observability practices.
• Drive cloud and Snowflake cost optimization through consumption monitoring, capacity planning, and engineering best practices.
• Lead and mentor a team of 3–4 data engineers, providing technical leadership, coaching, and career development.
• Serve as the technical subject matter expert for Snowflake, modern data platforms, and AI-ready data engineering practices.
• Define and enforce enterprise data engineering standards, architectural patterns, and development best practices.
• Collaborate with business stakeholders, product owners, analysts, architects, and data scientists to translate business objectives into scalable data solutions.

Lead Data Engineer, Snowflake

• 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

• Implement cloud-based scalable data platforms to support manufacturing operations
• Build and maintain Lakehouse and develop ELT/ETL workflows
• Integrate data from ERP and different source systems
• Configure and maintain Unity Catalog
• Monitor pipeline health, troubleshoot failures, and perform root-cause analysis
• Actively share knowledge and produce clear technical documentation

• Assemble trailer components and sub-assemblies
• Perform quality inspections of components and sub-assemblies
• Identify and perform repetitive assembly of various trailer component parts using blueprints or work instructions
• Safely utilize various measuring and inspection instruments
• Use various power and hand tools in a safe manner
• Work with a variety of materials, including steel, sheet metal, aluminum, solvents, oils and abrasives
• Recommend measures to improve production methods, equipment performance and quality standards
• Organize parts and equipment to maximize efficiency within department

Data Center Warehouse Associate – 1st Shift

• Lead strategy, prioritization, and roadmap alignment across multiple internal platform capability areas
• Own the strategy, roadmap, and evolution of internal data platform capabilities, including data ingestion, cross-cutting data services, the Enterprise Data Layer (EDL), data quality, metadata management, governance capabilities, and source onboarding
• Partner with Vizient Intelligence engineering, AI Delivery, architecture, security, and product leadership to define platform investment priorities
• Establish decision frameworks that sequence platform capabilities, address technical debt, improve platform reuse, enhance internal developer productivity, and support AI-assisted and agentic development workflows
• Align platform roadmaps with measurable outcomes, including reduced delivery friction, increased platform adoption, stronger governance, faster product delivery, and improved AI-first delivery maturity
• Drive platform capabilities that improve the reliability, accessibility, and reuse of enterprise data across Vizient Intelligence products
• Collaborate closely with data engineering teams to define platform capabilities that support scalable, high-quality, and governed data solutions
• Facilitate executive stakeholder alignment on platform direction, funding, dependencies, tradeoffs, adoption expectations, and AI-first operating model changes
• Represent platform product priorities in leadership forums by translating technical platform strategies into business-relevant decisions and resolving complex cross-functional priorities
• Guide and develop a small team of platform product professionals through coaching, mentorship, and professional development
• Establish lightweight platform product management standards, including discovery practices, roadmap narratives, success measures, prompt and context patterns, release planning, adoption planning, and responsible AI tool usage
• Build strong partnerships across engineering, AI Delivery, architecture, security, design, and internal product teams to enable consistent platform adoption and delivery outcomes

Data Engineer jobs in Texas

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Senior Data Engineer

Principal Data Engineer

Senior Engineer – Data

Curriculum Development Manager, Data Platforms

Data Engineer

Data Architect

Senior Data Engineer

Data Engineer II

Lead Data Engineer

Operations Engineer – Data Center, SysOps

Data Protection Engineer

Lead Data Engineer

Senior Technical Manager – Enterprise Data Platform

Senior Software Engineer, Data

Lead Data Engineer, Snowflake

Senior Software Engineer, ERP, Data & Domain

Senior Data Engineer

Senior Data Engineer

Data Center Warehouse Associate – 1st Shift

Lead Director, Platform Product Management – Data Platform & Operations