Principal Scheduling and Data Analytics Specialist
RTX
Principal Data and Analytics Engineer
Bizi Digital
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇺🇸 United States
Visit company websiteSalary
💰 $110,000 - $165,000 per year
Job Level
Lead
Tech Stack
AirflowBigQueryCloudGoogle Cloud PlatformKafkaPythonScalaSQL
About the role
- Help define and evolve enterprise data engineering blueprints, including data mesh, medallion architecture, and hybrid cloud data platforms.
- Set strategic direction for data platforms, tools, and services (e.g., Snowflake, GCP BigQuery, dbt, Kafka, Airflow/Prefect) in alignment with future-state architecture and business priorities.
- Architect and design highly scalable, resilient, cost optimal and secure data platforms.
- Lead the design and implementation of next-generation data platforms, ensuring fault tolerance, high availability, and optimal performance for petabyte-scale data.
- Establish and enforce organization-wide best practices for data pipeline development, CI/CD for data workflows, automated deployment playbooks, and robust rollback strategies.
- Lead technology evaluation and adoption, proactively researching, evaluating, and championing the integration of cutting-edge data technologies, frameworks, and methodologies.
- Define and scale enterprise knowledge management frameworks that ensure consistent documentation, discoverability, and reusability of data assets across domains.
- Establish and govern standards for metadata management, data lineage, architectural diagrams, and runbooks.
- Lead the design of federated governance models that empower domain-aligned teams to operate autonomously while conforming to centralized policies, frameworks and playbooks.
- Collaborate with data governance, compliance, and security teams to operationalize policy-as-code frameworks for data retention, access control, and PII handling.
- Advocate for and enable self-service knowledge discovery through tightly integrated cataloging tools (e.g., Alation, Collibra) and automated documentation generators.
- Ensure robust documentation and versioning standards are embedded in CI/CD workflows for pipeline code, transformation logic, and schema changes.
- Architect implementation of scalable, automated data quality frameworks that evaluate data at rest and in motion spanning completeness, timeliness, consistency, accuracy, and integrity.
- Lead integration of data quality rules, metrics, and health indicators directly into orchestration layers (e.g., Prefect, Airflow) and transformation frameworks (e.g., dbt).
- Evangelize a culture of data trust and transparency by integrating data quality insights into user-facing dashboards, alerts, and product health reports.
- Identify and promote enterprise-wide data opportunities through thought leadership, white papers, reference architectures, and innovation labs.
- Act as technical advisor to senior executives on data modernization, AI readiness, and platform consolidation strategies.
- Enable intelligent operations and decisioning by translating unstructured business logic into structured knowledge artifacts, such as KPIs, rulesets, feature stores, and semantic models used by dashboards or AI agents.
- Serve as a strategic translator between complex business challenges and modern data architecture by leading domain-level and cross-domain data product strategy engagements.
- Lead the design of enterprise-grade data products that align with OKRs, business transformation goals, and operational needs ensuring value realization across functional areas like supply chain, marketing, store ops, or customer satisfaction.
- Architect and operationalize a unified enterprise-wide semantic layer, metrics store, and business logic abstraction that powers dashboards, self-service analytics, and machine-readable APIs.
- Lead initiatives to unify KPIs, standardize metric definitions, and streamline business logic through reusable models.
- Design composable data assets and feature stores that enable real-time and offline access patterns for ML models, AI agents, and decision orchestration systems.
- Lead readiness initiatives for integrating data systems with LLM-powered agents and copilots, ensuring robust grounding data, latency optimization, and lineage tracking.
- Drive innovation in analytics automation, including anomaly detection, agent-triggered insights.
- Serve as champion for complex analytics transformations, ensuring technical feasibility, business value realization, and adoption.
- Drive culture change around data stewardship and accountability by embedding governance responsibilities into platform tooling and engineering workflows.
- Lead internal communities of practice, workshops, and code reviews to disseminate modern data practices.
- Mentor senior engineers across data and analytics engineering, elevating technical acumen and architectural judgment.
- Influence hiring and team design decisions, supporting the scaling of high-performing, and collaborative data teams.
- Represent the organization in external forums (conferences, meetups, technical alliances) and establish credibility as an industry thought leader.
Requirements
- Proven experience architecting enterprise-scale data platforms and ecosystems, including hybrid and cloud-native environments (e.g., GCP BigQuery, Snowflake, Iceberg, Advanced SQL, Erwin, dbt, Kafka, Alation, Collibra)
- Deep expertise in designing and scaling highly available, secure, and fault-tolerant batch and streaming pipelines with strong emphasis on cost optimization, observability, and latency control.
- Advanced proficiency in semantic modeling, reusable data asset design, and cross-functional data product delivery aligned to medallion architecture.
- Leadership in implementing CI/CD-enabled pipelines, RBAC frameworks, schema evolution strategies, and interoperable data exchange using Iceberg or equivalent table formats.
- Ownership of organization-wide metrics store and semantic layers, ensuring consistency, governance, and performance across reporting, AI, and ML use cases.
- Advanced expertise in programming languages such as Python, Scala, with the ability to architect complex data solutions.
- Demonstrated leadership in designing and overseeing the implementation of scalable, idempotent workflows using orchestration frameworks such as Airflow and Prefect.
- Demonstrated ability to translate business transformation goals into scalable data solutions and reusable patterns.
- Deep understanding of business processes, KPIs, and capability maps across functions such as supply chain, customer, store ops, and finance.
- Proven experience in driving cross-functional data product prioritization, influencing senior stakeholders, and quantifying impact of data initiatives.
- Experience shaping enterprise-wide data strategy by defining the long-term technical vision and architectural evolution roadmap across platforms, domains, and business units driving adoption of scalable, and governed data products.
- Experience leading platform modernization, tool evaluation, and architecture standardization across business domains.
- Expert competency in analytical and problem-solving that is crucial for identifying and resolving issues.
- Expertise in defining and enforcing enterprise-level data governance, metadata standards, and policy-as-code frameworks.
- Led the design and deployment of automated data quality management systems across ingestion, transformation, and consumption layers.
- Drive strategic KPI standardization by partnering with stakeholders, data stewards, and product teams to architect reusable semantic layers and metric definitions that enable trustworthy insights and LLM agent reasoning.
Benefits
- Competitive Wages & Paid Time Off
- Stock Purchase Plan & 401k with Employer Contributions Starting Day One
- Medical, Dental, & Vision Insurance with Optional Flexible Spending Account (FSA)
- Team Member Health/Wellbeing Programs
- Tuition Educational Assistance Programs
- Opportunities for Career Growth
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
data engineeringdata meshmedallion architecturecloud data platformsCI/CDdata quality frameworkssemantic modelingprogramming languages (Python, Scala)batch and streaming pipelinesdata governance
Soft skills
leadershipcollaborationmentoringstrategic thinkingcommunicationinfluencingproblem-solvinginnovationculture changecommunity building
