Data Analytics

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.

The **Distinguished Data Engineer** is the highest-level individual contributor (IC) data engineering role in a software or IT organization, accountable for the technical direction, integrity, and scalability of the enterprise’s data platforms and critical data products. This role exists to **design, standardize, and evolve** data engineering practices across domains, ensuring trusted, secure, cost-effective data foundations that power analytics, AI/ML, operational reporting, and customer-facing features.

The **Distinguished Business Intelligence Engineer** is a senior-most individual contributor (IC) who defines and scales enterprise-grade business intelligence capabilities—spanning metrics, semantic layers, analytics engineering patterns, and governed self-service analytics—so leaders and teams can make fast, correct, and trusted decisions. This role anchors the “last mile” of data: transforming curated data products into reliable insights experiences (dashboards, metrics, alerts, and decision workflows) with strong performance, usability, and governance.

The **Distinguished Analytics Engineer** is a top-tier individual contributor responsible for shaping how the organization models, defines, governs, and operationalizes analytical data for decision-making and customer-facing insights. This role operates at enterprise scale, designing durable metric systems, semantic layers, and analytics data products that are trusted, observable, and cost-effective.

A DataOps Engineer builds and operates the reliability layer for data products: the automation, deployment, observability, quality controls, and platform guardrails that keep data pipelines and datasets trustworthy in production. In a software or IT organization, this role exists to apply disciplined engineering practices (CI/CD, IaC, monitoring, incident management, SLOs) to the data ecosystem so that analytics, BI, and ML teams can move quickly without sacrificing correctness, security, or uptime.

The **Data Platform Engineer** designs, builds, and operates the shared data platform capabilities that enable reliable ingestion, storage, transformation, governance, and access to data across the company. The role focuses on creating scalable, secure, and cost-effective “paved roads” (standard patterns, infrastructure, tooling, and automation) so data producers and consumers can move faster with less risk.

The **Data Engineer** designs, builds, and operates reliable data pipelines and curated datasets that power analytics, reporting, and data-driven product features. The role converts raw, fragmented operational data into trusted, well-modeled, secure, and observable data assets that can be used at scale by analysts, data scientists, and product teams.

A Business Intelligence Engineer designs, builds, and operates the analytics layer that turns raw operational and product data into trusted, self-serve insights for decision-makers. The role sits at the intersection of data engineering, analytics, and stakeholder enablement—owning data modeling, metric definitions, dashboarding, and data quality controls that make analytics reliable and scalable.

The **Associate DataOps Engineer** supports the reliable, secure, and efficient operation of data pipelines, analytics platforms, and data products by applying DevOps-style engineering practices to data systems. This role focuses on day-to-day pipeline enablement, automation, monitoring, data quality controls, and incident response support—typically under the guidance of senior DataOps or Data Platform engineers.

The **Associate Data Platform Engineer** is an early-career individual contributor responsible for helping build, operate, and continuously improve the company’s data platform foundations—typically cloud-based storage, ingestion, orchestration, compute, and governance capabilities that enable analytics, reporting, and data products. This role focuses on reliable execution: implementing well-scoped platform features, maintaining pipelines and environments, monitoring jobs, troubleshooting incidents, and documenting operational practices under guidance from more senior engineers.

The **Associate Data Engineer** builds and operates the foundational data pipelines, datasets, and technical enablers that allow analytics, reporting, and data products to work reliably at scale. This is an **early-career, hands-on engineering role** focused on implementing well-defined pipeline tasks, improving data quality, and learning production-grade data engineering practices under the guidance of senior engineers.

The **Associate Business Intelligence Engineer** (Associate BI Engineer) builds and maintains reliable, well-modeled analytics assets—dashboards, reports, semantic layers, and curated datasets—that help teams make timely and accurate business decisions. This role sits at the intersection of data engineering, analytics, and stakeholder enablement, translating business questions into governed, performant, self-service BI solutions.

The **Associate Analytics Engineer** builds and maintains the trusted analytical datasets that power reporting, product insights, and decision-making in a software or IT organization. This role sits between data engineering and analytics: it transforms raw, ingested data into well-modeled, documented, tested, and reusable data assets for business intelligence (BI), product analytics, and operational reporting.

The Analytics Engineer designs, builds, and maintains trusted analytics-ready datasets, semantic models, and governed metrics that power dashboards, product analytics, and decision-making across the company. This role sits between Data Engineering and Analytics/BI, translating business questions into scalable data models while enforcing quality, documentation, and consistent definitions.