Data Engineer

• Build and operate the financial data plane for Numeric’s accounting data platform
• Develop scalable ingestion from NetSuite, Stripe, Brex, Ramp, HubSpot, S3 inboxes, and Postgres into ClickHouse
• Enforce data contracts and typed schemas at data boundaries
• Manage lakehouse schemas through declare, code generation, and apply pipelines
• Build query scoping and ACLs to protect customer data
• Develop batch processing and event-driven accounting workflows
• Improve data correctness, observability, ingestion health, re-ingestion, and traceability from ledger entries to raw events
• Build scalable reporting query patterns and materializations
• Collaborate with accounting module engineers, reporting teams, and AI-agent teams
• Learn the accounting domain and influence product direction while owning architecture end to end

Software Engineer, Data Platform

• Establish and validate connections between Talent Acquisition source systems and Snowflake
• Configure Power BI connectivity and scheduled refresh
• Implement data quality checks and refresh monitoring
• Build a query-ready dimensional data model in Snowflake
• Write and optimize SQL transformations for analysis-ready tables and historical reporting
• Develop Power BI dashboards covering executive summaries, requisition and pipeline health, funnel conversion, time-to-fill, time-in-stage, offer metrics, sourcing effectiveness, recruiter workload, and TA operational SLA metrics
• Build a governed semantic model with certified DAX measures
• Implement row-level security and optimize model performance
• Produce documentation covering pipeline architecture, data lineage, model diagrams, measure definitions, and troubleshooting
• Create end-user guides and deliver enablement sessions for TA Operations and HR leadership
• Partner with Talent Acquisition leadership to prioritize requirements and translate business questions into metrics
• Present dashboard walkthroughs and insights
• Deliver an operational Snowflake pipeline, dimensional model, Power BI dashboard suite, automated refresh monitoring, technical documentation, enablement materials, and a future analytics roadmap

Talent Acquisition Graduate Intern, Data Engineering & Analytics

• Define requirements and acceptance criteria for the platform's flagship products: single-source-of-truth tables for clusters, associated components, customers, and workloads, along with relationship tables
• Partner with source and collector teams across DGX Cloud to onboard data and define versioned data contracts covering schema, semantics, freshness, quality, and change notification
• Drive identifier and schema standardization upstream with producing teams, recording, owning, and tracking each standardization decision to closure
• Work with infrastructure security, compliance, and enterprise data teams to define the security model
• Engage with engineering leadership to establish priorities and resource allocation, sequence delivery against firm external constraints, and gain prioritization consensus across collaborating teams
• Define and report measures including standardization debt per source, pipeline freshness and quality SLOs, source onboarding throughput, and time to first answer for a new consumer
• Define requirements and build the case for extending the platform to NVIDIA Cloud Partners and cloud service providers

Senior Data Platform and Analytics Product Manager – DGX Cloud

• Develop privacy-preserving mechanisms, derived signals, and shared semantics for cross-border data products
• Build cross-border entity resolution as a foundational capability
• Collaborate with data science teams on matching approaches and match quality
• Build semantic and ontology layers that make data consumable by AI agents across regions
• Implement governed discovery and compliant access paths
• Guide technology adoption through reference implementations and reusable engineering patterns
• Lead prototyping and R&D efforts with product and engineering teams
• Mentor engineers in fundamentals thinking, system design, and product awareness
• Report to the Sr. Director of Platform Engineering as an individual contributor

Staff Architect – Data Platform

• Own end-to-end designs shaping the Security Data Platform architecture on Databricks and AWS using Spark, Delta Lake, and Unity Catalog
• Translate ambiguous needs into design proposals, implementation plans, and production capabilities
• Build and evolve distributed batch and streaming pipelines for EDR telemetry, network logs, identity events, cloud audit logs, and other security data sources
• Write and review production Python and SQL code, develop platform features, refactor unreliable code, and establish engineering patterns
• Lead code and design reviews and own test strategy and automation choices
• Build and maintain tested Terraform modules for AWS and Databricks infrastructure, including S3, IAM, VPC networking, and MWAA
• Automate delivery through version control and CI/CD
• Design layered lakehouse data models and normalization patterns, including OCSF mappings where appropriate
• Establish data contracts, quality checks, cataloging, classification, lineage, access controls, and retention policies
• Engineer and operate Airflow and MWAA orchestration, including dependencies, retries, backfills, alerting, and automated recovery
• Define service level objectives and instrument data freshness, completeness, pipeline health, and query performance
• Diagnose and resolve complex production issues across Spark, storage, orchestration, and cloud infrastructure
• Improve incident response, runbooks, recovery testing, and operational readiness
• Lead measurable cost and performance improvements through query and Spark tuning, storage lifecycle policies, partitioning, and compute right-sizing
• Lead cross-team technical discussions and communicate decisions, risks, and business impact
• Partner with detection and security analytics engineers on SIEM modernization and automated detection deployment
• Mentor engineers through pairing, technical reviews, documentation, and knowledge sharing
• Evaluate and introduce platform innovations, including AI-assisted engineering and operational automation

Staff Security Data Engineer

• Own the data platform end-to-end, including ingestion, transformation, storage, query, and access
• Drive the data platform roadmap as company data needs grow
• Build and evolve batch and streaming pipelines using PySpark/EMR, Kinesis, Lambda, and Step Functions
• Ingest data from Postgres, Salesforce, third-party vendors, and product event streams into an Iceberg-based lake
• Design SCD tables, event tables, and data conventions for engineers and analysts
• Partner with product, engineering, analytics, and operations stakeholders to turn data requests into reliable pipelines
• Write documentation and tooling that enable self-service
• Own data-system security and compliance, including audit logging, access control, and temporary-access workflows
• Optimize backend query performance through read-replica routing, indexing, caching, and I/O instrumentation in Java/Spring services
• Investigate and remediate IOPS spikes, pipeline failures, schema drift, and late data
• Use AI for pipeline scaffolding, schema work, and investigations; ship internal AI tooling
• Mentor engineers and analysts on working with the data platform

Senior Data Engineer

• Build and maintain marketing attribution systems, including UTM tracking, user journeys, and conversion mapping
• Implement and manage first-party data passback to ad platforms, including conversions, revenue, and LTV signals
• Configure and maintain GTM, Google Ads Enhanced Conversions, Meta Pixel and Conversions API, LinkedIn, TikTok, and other ad-platform tracking infrastructure
• Set up and maintain conversion events and schemas across platforms
• Ensure consistent event definitions and naming across product, CRM, and ad platforms
• Build and manage audience sync pipelines
• Integrate CRM and warehouse data into ad platforms for optimization, including offline conversions, lead stages, and revenue
• Operationalize marketing experiments, including UTMs, tracking parameters, and campaign setup
• Debug and resolve tracking and attribution discrepancies across ads, CRM, and warehouse systems
• Build and validate marketing and pipeline dashboards
• Implement privacy-safe tracking practices, including Consent Mode and CMP integrations
• Partner with growth, product, and data teams to support decision-making

Growth Data Engineer

• Design, build, deploy, and maintain scalable batch and event-driven data pipelines
• Ingest data from application databases, SaaS tools, and external APIs into BigQuery using Fivetran, custom Python loaders, and orchestration tooling
• Design and implement dimensional and analytical data models in dbt using raw, staging, and marts layers
• Implement testing, monitoring, alerting, and data contracts across pipelines
• Define and meet freshness and accuracy SLAs
• Triage and resolve pipeline failures and data incidents to root cause
• Tune warehouse queries, partitioning, and clustering
• Manage BigQuery spend and pipeline efficiency as data volume grows
• Establish version control, code review, CI/CD, and infrastructure-as-code best practices
• Document systems and runbooks
• Implement access controls, PII handling, and data retention practices
• Partner with Security and Legal on compliance requirements
• Partner with product engineers on source schema design and change management
• Translate business questions into reliable datasets, metric definitions, and self-serve reporting in Metabase
• Maintain the semantic layer, metric definitions, and documentation supporting accurate LLM-based tools and internal agents

AI Data Engineer, Data Platform

• Own the discovery and access side of Agilent's scientific data platform
• Define the target state and roadmap for scientific data discovery and access
• Establish catalog and access behavior, service boundaries, contracts, integration patterns, and the path between them
• Own and version catalog records, subgraph boundaries, data service views, and event schemas
• Define authoritative systems under data governance rules
• Design controlled, provenance-aware access paths for AI agents
• Review integrations against contracts and participation requirements
• Design modernization paths for legacy sources, including on-premises, hybrid, and cloud transitions
• Set query and index performance targets
• Design pipelines keeping catalogs, search indexes, and access views current, including backfill and recovery
• Work directly with engineering on designs, implementation plans, and delivered work
• Create decision records and diagrams and explain tradeoffs to technical and business stakeholders
• Work with .NET/C#, Angular, federated GraphQL, PostgreSQL, Oracle, SQL Server, RabbitMQ, Kubernetes, Helm, Windows installers, S3-compatible storage, and scientific data formats

Senior Data Architect, Scientific Data Platform

• Design, develop, and maintain scalable data pipelines and ETL workflows
• Build, automate, and optimize data infrastructure for analytics, reporting, and machine learning applications
• Ensure data quality, consistency, and security across all sources and sinks
• Collaborate with engineering, analytics, and product teams to define data requirements and deliver reliable datasets
• Implement monitoring solutions and proactively resolve data pipeline issues
• Optimize storage and data processing performance for growth and efficiency
• Contribute to data modeling efforts and schema design for analytics and product needs
• Help establish best practices and empower a data-driven culture across the organization