Job Details:

The Senior IT Data Engineers are experts in data streaming, building data pipelines that support real-time data refreshes and are cost-optimized for computing resources. They deeply understand data security, implementing row-level and column-level security measures. This role typically involves leading the implementation of complex projects, using advanced big data technologies, and ensuring robust data pipeline orchestration across multiple systems.

Essential Duties and Responsibilities

• Own and drive the overall data strategy, including the multi-quarter technical roadmap, platform architecture, and data engineering standards.

• Architect end-to-end data solutions across cloud platforms (AWS, GCP, or Azure), setting standards for orchestration (Airflow,Dagster), transformation (dbt), streaming (Kafka, Flink), and storage (Delta Lake, Iceberg, Snowflake, BigQuery).

• Own the enterprise data modeling strategy 26#8212; crafting scalable models using dimensional, multi-dimensional, and advanced normalization techniques, with enterprise-wide documentation and metadata governance.

• Define API design standards and data contracts to ensure reliable, well-governed interfaces between data producers and consumers.

• Establish enterprise-level data governance, security, and compliance frameworks across all data and AI systems, including access controls, cataloging, and lineage.

• Define and enforce CI/CD standards for data pipelines, containerized architectures (Docker, K8s), andinfrastructure ascode (Terraform).

• Drive data observability practices and platform reliability at enterprise scale.

• Drive build-vs-buy evaluations for data and AI tools, considering TCO, vendor lock-in, scalability, and organizational fit; manage vendor relationships.

• Own or co-own infrastructure budget and capacity planning for data platformresources;optimizecloud costs at the organizational level.

• Define and drive the organization's agentic AI strategy, architecting enterprise- scalemulti-agent systems, autonomous data pipelines, and RAG/knowledge graph platforms.

• Establish AI governance frameworks, including ethics policies, bias detection, safety guardrails, security standards (prompt injection, data exfiltration, PII), and compliance with emerging regulations (e.g., EU AI Act).

• EstablishLLMOpspractices at scale 26#8212; model deployment, prompt versioning, A/B testing, performance monitoring, drift detection, and cost optimization.

• Design human-in-the-loop escalation paths for critical AI-driven decisions, ensuringappropriate oversight.

• Lead AI platform evaluation and integration, including TCO analysis, data residency, and SLA requirements for agentic frameworks.

• Set software engineering best practices 26#8212; code review standards, design patterns, technical debt management, and documentation.

• Advocate for and lead adoption of data mesh and data-as-a-product principles.

• Mentor the engineering team on data engineering, data modeling, and AI best practices.

• Perform other assigned job-related duties that align with our organization's vision, mission, and values and fall within your scope of practice.

Qualifications

Education:Bachelor's Degree or relevant experience.

Preferred Certification(s):AWS Solutions Architect Professional, Google Professional

Data Engineer, Azure Solutions Architect Expert, Databricks Certified Data Engineer

Professional, or equivalent.

Experience:3+ years of relevant and practical experience.

Special Skills

• Expertproficiencyin Python and SQL for data engineering at scale.

• Expertisein modern data platforms (Databricks, Snowflake, BigQuery),lakehousearchitectures(Delta Lake, Iceberg), and streaming (Kafka, Flink, Pub/Sub).

• Deepexpertisein at least one major cloud platform with cross-cloud awareness.

• Mastery of orchestration, transformation (dbt), containerization (Docker, K8s), andIaC(Terraform).

• Advanced enterprise data modeling, warehousing, data contracts, and API design.

• Expertisein CI/CD, data observability, governance, data mesh, and platform reliability.

• Experience in technical roadmap ownership, build-vs-buy evaluation, and budget/capacity planning.

• Expert-level knowledge of agentic AI architectures,LLMOps, RAG, knowledge graphs, and AI governance/safety/security.

Soft Skills

• Leadership:Owning and driving data and AI strategy across the organization.

• Strategic Vision:Translating businessobjectivesinto actionable technical roadmaps.

• Stakeholder Management:Building relationships with partners and executive leadership.

• Communication:Presenting complex data and AI concepts to board-level audiences.

• Mentorship:Developing the data engineering team's data and AI competencies.

• Decision-Making:Making high-impact choices on architecture, platforms, and investments.

• Change Management:Guiding the organization through data and AI transformations.

• Innovation & Thought Leadership:Driving industry best practices in data engineering, modeling, and agentic AI.

• Negotiation:Balancing technical requirements with business needs and resource constraints.

**Not eligible for visa sponsorship **

** Not eligible for relocation assistance **

Relocation Assistance Eligible:

Work Shift: