Data Engineer

• Own data architecture end-to-end. Define how we capture, model, and serve critical business data—then implement it in production.
• Build mission-critical pipelines. Develop and operate batch data workflows that process high-volume events related to notifications with tight guarantees for latency, completeness, and accuracy.
• Design and implement canonical models. Create domain-oriented data models that serve as the source of truth for analytics, ML, and production applications.
• Enforce data quality at scale. Build tests, lineage, monitoring, and reconciliation systems that make every dataset observable and every anomaly actionable.
• Automate operational workflows. Partner with business systems and platform teams to eliminate manual data handoffs and reconcile data across services, warehouses, and external systems.
• Enable insights and experimentation. Support analytics, ML, and product engineering teams by exposing high-quality, self-healing, maintainable data assets.

Data Engineer, Notifications

• Works with customers, business, and engineering departments to translate business objectives into system requirements.
• Provides technical advisory services from design, development, and DevOps for implementations containing multiple integrations.
• Drafts technical engineering plans, designs, and systems integration-related documents.
• Defines estimated time frames, quality, and quantity of resources required for technology projects.
• Collaborates with the Product team for feedback on platform features and functions.
• Delivers new technical projects for expansion and upsells.

AI Data Architect

• Own the analytics data layer. Design and operate the ClickHouse-backed warehouse that powers operational, business, and autonomy reliability metrics, partnering with the Platform team that owns ingestion.
• Define the metrics that matter. Partner with operations, product, engineering, and finance to turn fuzzy questions into trustworthy metrics: autonomy uptime, intervention rates and classifications, release-over-release reliability, mission success, fleet utilization, customer ROI.
• Ship customer-facing data experiences. Build React dashboards inside our web and mobile customer analytics portals, partnering with the Applications & Connected Services team.
• Power internal decision-making. Build the Grafana boards, custom React dashboards, and self-serve tooling that let teams answer their own questions and catch problems before customers do.
• Be accountable for accuracy. Establish the data quality, validation, lineage, and documentation that make every number defensible. When someone asks where a metric came from, you have an answer.
• Share ownership of data contracts with the Platform and Cloud software teams.

Lead Data Engineer, Analytics & Insights

• Join a team of talented engineers working closely with Data Scientists to build and scale our next-generation Ad EnGage data pipeline.
• Measure and optimize the effectiveness of advertising by combining and analyzing a wide range of datasets.
• Work with large-scale datasets using a modern data stack centered on AWS, Airflow, dbt, and Snowflake.
• Contribute to building reliable, high-quality data pipelines and improving the performance, scalability, and observability of our data platform.

• Architecting high-scale, secure and interoperable data platforms across the enterprise to enable AI and analytics applications
• Architecting data domains and designing data products to be shared across the enterprise
• Architecting cross-segment semantic layer and data catalog solutions
• Designing an agentic architecture for exposing data from enterprise data lakehouse domains to AI applications
• Developing MDM strategy and architecting MDM solutions for complex enterprise domains (e.g., Finance, HR)

Lead Data Architect

• Contribute to a culture of technical collaboration and scalable development
• Contribute ideas and direct the team’s investment to impactful, data-driven directions
• Be part of a team which has high visibility across the organization

• Define and evolve enterprise data architecture principles, standards, and reference architectures aligned to business priorities and modern practices
• Lead the design and maintenance of enterprise conceptual, logical, and physical data structures, ensuring consistent definitions and shared semantics
• Establish domain-oriented architecture patterns for reusable, governed data products and interoperable data flows
• Develop architecture patterns supporting both batch and streaming workloads, including event-driven approaches and canonical representations
• Partner with data engineering teams to translate architecture and logical designs into scalable physical implementations on AWS cloud data platforms
• Ensure consistent data definitions, lineage, and metadata documentation across domains by enabling cataloging and lineage platforms
• Maintain a central repository of architecture artifacts, definitions, and metadata documentation
• Influence data design decisions across programs and projects through architecture reviews and standards enforcement
• Facilitate architecture and data design workshops with business and technical stakeholders
• Lead, mentor, and develop a team of data architects and specialists, ensuring consistent delivery of architecture outcomes
• Stay current on evolving architecture trends and evaluate their applicability to Deckers

Director, Data Architecture

• Define and drive technical strategy for Data Platform
• Develop and execute multi-quarter roadmap
• Architect for the AI-Native future
• Drive evolution to near-real-time data availability
• Spearhead builder mindset
• Drive cross-functional alignment

Senior Staff Software Engineer, Data Platform

• Create a shared vision and cohesive roadmap for HubSpot's data primitives — objects, properties, property groups, associations, and the metadata systems that make them discoverable and extensible
• Own the API mission for Data Platform: define standards for how our data layer is surfaced to developers, set the direction for API quality and consistency, and coordinate with Ecosystem and Partnerships teams on where API capability unlocks platform value
• Drive alignment on schema evolution across HubSpot — working with PMs, engineers, and architects across Marketing, Sales, Service, and AI Agents to ensure the data model grows in a way that's backward-compatible and forward-looking
• Guide new product ideas from initial concept all the way through launch, including shipping improvements to how customers define and manage their own data model in HubSpot
• Understand our customers and their data complexity by engaging with them directly — from SMBs managing simple contact records to enterprise teams running sophisticated multi-object data architectures
• Use data and developer research to identify friction in our API surface, devise strategy to address it, and partner with engineering to ship improvements that measurably improve developer experience
• Represent the data primitives and API roadmap to senior leadership, Ecosystem partners, and key enterprise customers

Senior Product Manager, Data Platform, Platform API

• Lead the design and evolution of the data platform architecture, establishing patterns and standards the team builds on.
• Build and operate production-grade data pipelines that ingest and transform high-variance, real-world clinical data reliably and at scale.
• Design for automation from the start: pipelines that detect problems, recover gracefully, and surface issues without requiring manual intervention to run.
• Contribute to quarterly data product releases, working closely with product, clinical, customer success teams to meet commitments.
• Build data quality tests that reflect the evolving needs of our downstream consumers.
• Mentor and elevate other data engineers through code review, architecture decisions, and shared standards.
• Actively use and advocate for AI tools that improve the team's development velocity and code quality.

Senior Data Engineer

• Build and maintain high‑performance streaming and batch data pipelines that power AI applications
• Implement and extend embedding generation workflows, vector store integrations, and retrieval pipelines supporting semantic search, RAG systems, and AI assistants
• Develop and optimize scalable storage and retrieval patterns, focusing on cost‑efficient architecture and smooth production performance
• Implement AI‑optimized data models and storage patterns that align with broader enterprise architecture and platform requirements
• Integrate pipelines with shared AI platform services (agent frameworks, registries, feature stores), ensuring clean, versioned, and reliable data delivery
• Build reusable ingestion, transformation, and data processing components that streamline adoption across engineering teams
• Embed end‑to‑end observability into data systems, including metrics, structured logging, automated alerts, drift detection, and failure analysis
• Ensure deployed pipelines meet high standards for reliability, recoverability, auditability, and long‑term maintenance
• Drive execution by owning the full development lifecycle: prototyping, implementation, testing, deployment, optimization, and documentation
• Collaborate closely with infrastructure, ML engineering, product, and governance teams to deliver production‑ready AI capabilities
• Lead by example through strong execution, high‑quality code, and proactive problem solving

• Serves as a lead in the analysis of business requirements associated with the design and implementation of highly complex, highly scalable enterprise data processes
• Designs and develops robust and efficient processes for extracting, loading and transforming data to meet the needs of enterprise scale projects
• Provides guidance and participates in activities involved in the analysis, design, development, troubleshooting, debugging and maintenance of an enterprise data management and data ecosystem
• Identifies, investigates, tracks, and resolves the most complex technical errors ensuring the solution is timely and of acceptable quality
• Establishes and enforces database standards, guidelines, procedures and quality assurance for database deliverables such as conceptual design, logical data model capacity planning, external data interface specs, data loading, data maintenance plan and security
• Reviews and analyzes data objects and models to improve data management and data access
• Designs, constructs, tests and implements extraction and transformation programs, processes and cycles for historical and current source data
• Creates technical specification for supporting enterprise data transformation processes and supplementing system documentation for ETL processes
• Performs data profiling, source-target mappings, data transformation development, optimization, testing and implementation
• This position participates and serves as a lead in maintaining the ETL environment for the company’s system-wide enterprise data warehouse.

Supply Chain Data Engineer

• Design and implement data models, schemas, and ontologies for chemical, biological, and automation-generated data that serve discovery workflows across the portfolio.
• Define and maintain controlled vocabularies, metadata standards, and FAIR-compliant data frameworks in partnership with Preparedness4Insight.
• Implement semantic data standards (RDF, OWL, SPARQL) and ontology engineering practices to create interoperable, machine-readable scientific data.
• Design and implement data lakehouse architecture using modern platforms (Databricks, Snowflake, or equivalent).
• Build and optimize ETL/ELT pipelines using Spark, dbt, or similar tools.
• Implement real-time and streaming data integration (Kafka, Kinesis) connecting LIMS, instruments, and lab automation systems to the data infrastructure.
• Design and implement knowledge graphs (Neo4j, Amazon Neptune, TigerGraph) that capture molecular, target, pathway, and experimental relationships across the discovery landscape.
• Architect specialized data solutions: array databases (TileDB) for genomics/imaging, document stores (MongoDB) for experimental records, and vector databases for embedding-based retrieval supporting ML and RAG workflows.

Advisor – Data Architect, Data Foundry

• Own the roadmap for the CSS data platform
• Establish architectural principles and governance standards
• Drive convergence across product analytics, ML infrastructure, and AI data foundations
• Represent data engineering at the executive level
• Set and uphold the technical bar for distributed data systems
• Lead and grow multiple engineering teams
• Build a culture of ownership and accountability
• Build deep partnerships across various teams
• Influence Salesforce-wide standards for data and AI engineering

Director, Data Engineering – Customer Success Score

• Define and drive the technical roadmap across data platform, analytics engineering, and ads data infrastructure. Set the architectural vision for how data is ingested, transformed, modeled, and served at Life360.
• Own the analytics engineering strategy end-to-end: dbt project structure, data modeling standards (dimensional, OBT, and semantic layer), testing and documentation practices, and the development workflow that analytics engineers use daily.
• Oversee the data platform: Databricks infrastructure, compute optimization, pipeline orchestration, data lake architecture, and the reliability/observability stack that keeps it all running at consumer scale.
• Drive toward a self-serve data experience where analysts and data scientists can answer their own questions without engineering bottlenecks—this is the outcome that ties platform and analytics engineering together.
• Make strategic build vs. buy decisions across the data stack and manage vendor relationships (Snowflake, Databricks, Amplitude, and related tooling).
• Drive data quality, governance, and documentation standards that make data trustworthy and self-service across the company.
• Bring an AI native approach to data engineering: leverage AI tools to accelerate development cycles, evaluate AI-powered data quality and anomaly detection solutions, and ensure our data infrastructure supports ML/AI workloads and experimentation at scale.
• Stay current on emerging technologies in the data and AI space and make pragmatic decisions about adoption—knowing when a new tool solves a real problem vs. when it’s a distraction.

Director, Data Engineering – AI Native

• Lead the Architectural vision for the Product Data platform
• Drive complex architectural decisions and technology innovation in OLTP systems infrastructure
• Build and evolve the knowledge retrieval engineering organization to deliver predictable execution, high quality, and operational excellence
• Serve as a senior leader within the Product Data leadership team, shaping organizational strategy, prioritization, execution, and innovation across the portfolio
• Partner with product, platform, and ecosystem teams to drive adoption of the Product Data platform across Autodesk’s product portfolio

Senior Director, Data Architecture and Engineering

• Shape the future of data and analytics capabilities at Walmart Global Tech.
• Lead a diverse team of data engineers distributed globally.
• Drive the Data engineering charter, solve key enterprise problems for multiple markets.
• Coordinate across retail value chain functional teams on value realization initiatives.
• Develop and leverage a consistent framework to identify and prioritize use cases.
• Solve complex business and operational data problems.
• Lead the team to assess customer needs and translate business problems into analytical problems.
• Establish platform best practices and drive the use of state-of-the-art techniques.
• Manage development and support of data commercialization platform for real-time and batch data processing.
• Build and lead a high-performing team of data engineers.
• Drive continuous improvement by researching industry best practices.

Group Director, Data Architecture – Engineering

• Design and Scale Data Infrastructure: Architect and maintain scalable, reliable data pipelines and systems across our stack.
• Drive Data Quality and Governance: Build frameworks and standards that ensure data is accurate, well-documented, and trustworthy at every layer.
• Evolve Our Platform: Continuously evaluate and improve our solutions - optimizing for performance, cost, and scalability while bringing best practices and a forward-thinking approach to every decision.
• Enable the Business: Partner with Data Science, Analytics, and business teams to develop pipelines, data models, metric definitions and semantic layers and ensure the data is consistent and accurate.
• Balance Speed and Scale: Recognize when to move fast with a pragmatic solution and when to invest in building the right system - and communicate those tradeoffs clearly to stakeholders.
• Champion Modern Practices: Embrace AI-assisted development workflows, stay current on tooling, and help the team continuously raise the bar.

• Designing and developing modern, data-centric applications that support clinical and operational workflows
• Building scalable data pipelines, transformation and enrichment processes, and intuitive user interfaces
• Mentoring junior Data Engineers

Data Engineer I

• Collaborate with cross-functional teams to understand business requirements and translate them into effective cloud-based data architecture solutions.
• Design data models, data flow diagrams, and schema structures that optimize data storage, retrieval, and processing on cloud platforms.
• Design and develop data integration strategies to consolidate data from diverse sources into a unified and coherent format.
• Evaluate and select appropriate cloud services and tools for various data-related tasks.
• Work closely with data engineers, data scientists, and other stakeholders to understand their needs and provide architectural guidance.
• Responsible for overseeing the successful execution of data projects and ensuring the outcomes meet business objectives and technical requirements.
• Lead and mentor junior team members, promoting knowledge sharing and continuous learning.

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Data Engineer, Notifications

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Data Engineer

Data Engineer I

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