The **Staff DataOps Engineer** is a senior individual contributor responsible for the reliability, scalability, security, and operational excellence of the organization’s data platform and data delivery lifecycle. This role establishes and evolves the **DataOps operating model**—CI/CD for data, orchestration standards, observability, incident response, data quality controls, and cost governance—so analytics, product, and ML teams can ship trusted data products quickly and safely.

The **Staff Data Platform Engineer** is a senior individual contributor who designs, builds, and operates the shared data platform capabilities that enable reliable analytics, data products, and ML workloads at scale. This role combines deep hands-on engineering with architectural leadership—owning critical platform components (ingestion, storage, compute, orchestration, governance, and observability) and setting technical direction across multiple teams.

The **Staff Data Engineer** is a senior individual contributor (IC) responsible for designing, building, and evolving the company’s data platform and high-impact data products so analytics, AI/ML, and operational use cases are reliable, secure, and scalable. This role blends hands-on engineering with technical leadership: setting patterns and standards, driving cross-team alignment, and unblocking complex delivery across the data ecosystem.

The Staff Business Intelligence Engineer is a senior individual contributor in the Data & Analytics organization responsible for building and scaling trusted, performant, and governable analytics solutions—especially the semantic layer, curated datasets, and critical dashboards that drive executive and operational decisions. This role combines deep BI engineering craft (data modeling, dashboard and semantic design, performance tuning) with staff-level technical leadership (standards, cross-team alignment, and mentoring).

The Staff Analytics Engineer designs, builds, and governs the organization’s trusted analytics data foundation—turning raw operational data into well-modeled, well-documented, and high-quality datasets and metrics that power decision-making, experimentation, and customer/product insights. This role sits at the intersection of data engineering and analytics, with a staff-level mandate to define standards, uplift the analytics engineering practice, and reduce friction from “data creation” to “data consumption.”

The **Senior DataOps Engineer** designs, builds, and continuously improves the operational backbone that keeps data products reliable, secure, observable, and deployable at speed. This role applies DevOps/SRE-style engineering rigor to data pipelines, lakehouse/warehouse platforms, and analytics/ML workflows—focusing on automation, testing, CI/CD, monitoring, incident response, and governance-by-design.

The **Senior Data Platform Engineer** designs, builds, and operates the core data platform that enables trusted, secure, scalable analytics and data products across the organization. This role focuses on the platform capabilities—ingestion, storage, processing, orchestration, governance, and observability—so that data engineers, analysts, data scientists, and product teams can reliably deliver business outcomes with minimal friction.

The Senior Data Engineer designs, builds, and operates reliable, secure, and scalable data pipelines and data platform components that enable analytics, reporting, experimentation, and downstream data products. This role converts raw operational data into governed, high-quality, well-modeled datasets that are easy to discover, trust, and use across the organization.

The **Senior Business Intelligence Engineer** designs, builds, and operates trusted analytics assets—semantic layers, curated datasets, dashboards, and reporting services—that enable leaders and teams to make fast, accurate, and repeatable decisions. The role blends analytics engineering and BI platform engineering: turning raw, distributed data into governed, high-performing, self-service insights.

The Senior Analytics Engineer designs, builds, and operationalizes high-quality analytical data models and trusted metrics that power decision-making across a software or IT organization. This role sits at the intersection of data engineering and analytics, translating business questions into scalable, governed datasets and semantic layers that enable reliable self-service analytics.

The **Principal DataOps Engineer** is a senior individual-contributor (IC) responsible for designing, standardizing, and continuously improving the operational backbone of the organization’s data platform—ensuring data pipelines, orchestration, environments, and data products are **reliable, observable, secure, cost-efficient, and delivery-friendly**. This role blends deep data engineering knowledge with DevOps/SRE practices to reduce failure rates, shorten lead times, and raise trust in data across analytics, BI, and ML use cases.

The **Principal Data Platform Engineer** is a senior individual contributor who designs, evolves, and operationalizes the enterprise data platform that enables reliable, secure, and scalable analytics, ML/AI, and data-driven product capabilities. This role sets technical direction for data infrastructure, establishes engineering standards, and solves the highest-complexity platform problems spanning ingestion, storage, processing, governance, and serving.

The Principal Data Engineer is the senior-most individual contributor (IC) data engineering role responsible for setting technical direction, designing durable data platform architectures, and ensuring reliable, secure, and scalable data products that power analytics, reporting, and machine learning. This role combines deep hands-on engineering with cross-team technical leadership—driving standards, patterns, and platform evolution while tackling the company’s hardest data integration, modeling, and reliability problems.

The **Principal Business Intelligence Engineer** is a senior individual contributor responsible for designing, building, and governing the enterprise BI ecosystem—spanning semantic models, metrics definitions, dashboards, analytics enablement, and performance/reliability of BI delivery. This role translates complex business questions into trusted, scalable analytics products while setting technical direction and standards for BI engineering across the Data & Analytics organization.

The Principal Analytics Engineer is the senior-most individual contributor responsible for designing, governing, and evolving the company’s analytics data foundations—turning raw, operational data into trusted, well-modeled, well-documented, and high-performing analytics datasets and metrics that business teams can use with confidence. This role sits at the intersection of data engineering, analytics, and product thinking, with a strong emphasis on semantic consistency, scalable modeling patterns, and measurable data quality.

The Lead DataOps Engineer is accountable for the reliability, scalability, and operational excellence of the organization’s data delivery systems—pipelines, orchestration, environments, testing, observability, and release processes that move data from sources to trusted, consumable datasets. This role applies DevOps/SRE principles to data and analytics, ensuring that data products are delivered with predictable quality, clear service levels, and automated controls.

The Lead Data Platform Engineer designs, builds, and operates the shared data platform that enables reliable, secure, and scalable analytics and data products across the organization. This role blends hands-on engineering with technical leadership—setting platform direction, establishing standards, and unblocking delivery for multiple teams that produce or consume data. It exists in software and IT organizations because high-quality analytics, AI/ML, and operational reporting require a robust platform layer (ingestion, storage, transformation, governance, and observability) that product teams should not have to reinvent repeatedly.

The **Lead Data Engineer** is a senior technical leader within the **Data & Analytics** department responsible for designing, building, and operating reliable, secure, and scalable data pipelines and data platform capabilities that enable analytics, reporting, experimentation, and data-driven product features. This role combines hands-on engineering with technical leadership—setting standards, guiding architecture, mentoring engineers, and aligning delivery with business priorities.

The Lead Business Intelligence Engineer is accountable for designing, building, and operating trusted analytics datasets, semantic layers, and executive-ready dashboards that enable high-quality decisions across the company. This role combines deep technical capability (data modeling, BI performance engineering, governed metrics, and reliable delivery) with leadership responsibilities (standards, mentorship, stakeholder alignment, and roadmap ownership for BI).

The **Lead Analytics Engineer** designs, builds, and governs the analytics data layer that turns raw operational data into trusted, performant, and reusable datasets for reporting, experimentation, and decision-making. This role sits at the intersection of data engineering, BI, and business analytics, and is accountable for the quality and usability of analytical data products (e.g., curated marts, semantic models, metrics layers) that power dashboards, self-serve exploration, and product analytics.

The Junior DataOps Engineer supports the reliability, automation, and operational excellence of the company’s data pipelines and analytics platform. This role focuses on building and maintaining repeatable, observable, and well-controlled data operations—helping ensure that data products (dashboards, datasets, features, and reports) are delivered accurately and on time.

The **Junior Data Platform Engineer** supports the build, operation, and continuous improvement of the company’s data platform foundations—ingestion, orchestration, storage, transformation frameworks, and reliability guardrails—so analytics and data products can be delivered safely and consistently. The role focuses on implementing well-scoped changes, maintaining pipelines and platform components, and improving observability, quality, and automation under the guidance of more senior engineers.

The **Junior Data Engineer** builds, maintains, and monitors foundational data pipelines and datasets that enable reliable analytics, reporting, and data-driven products. The role focuses on implementing well-defined data integration tasks, improving data quality and observability, and contributing to a scalable data platform under guidance from senior engineers.

The **Junior Business Intelligence Engineer** builds and supports the analytics assets that enable reliable reporting and decision-making across a software or IT organization. This role focuses on **transforming raw data into trusted datasets, metrics, and dashboards**, while adhering to established engineering patterns, data governance practices, and quality standards.

The **Junior Analytics Engineer** designs, builds, tests, and maintains curated analytics datasets (often called “models” or “data marts”) that enable trusted reporting, self-service BI, and product/business decision-making. Working under the guidance of senior analytics engineers and/or data engineers, this role converts raw and semi-structured data into well-documented, quality-checked, and stakeholder-friendly tables and metrics.

The **Distinguished DataOps Engineer** is a top-tier individual contributor (IC) who designs, standardizes, and continuously improves the operating system for reliable, secure, and scalable data delivery across the enterprise. This role blends deep data engineering, SRE/DevOps discipline, platform thinking, and governance-by-design to ensure that data products (pipelines, transformations, models, and semantic layers) are **deployable, observable, testable, and recoverable**.

The **Distinguished Data Platform Engineer** is a top-tier individual contributor responsible for defining, evolving, and operationalizing the enterprise data platform strategy that powers analytics, AI/ML, and data-driven products. This role designs durable platform architectures, sets engineering standards, and resolves the most complex scalability, reliability, governance, and cost challenges across the data ecosystem.