Senior IT Data Engineer - Python/SQL (Onsite)

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., * 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 - 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), and infrastructure as code (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 platform resources; optimize cloud costs at the organizational level.

• Define and drive the organization's agentic AI strategy, architecting enterprise- scale multi-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).

• Establish LLMOps practices at scale - 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, ensuring appropriate oversight.

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

• Set software engineering best practices - 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.

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

• Expert proficiency in Python and SQL for data engineering at scale.

• Expertise in modern data platforms (Databricks, Snowflake, BigQuery), lakehouse architectures (Delta Lake, Iceberg), and streaming (Kafka, Flink, Pub/Sub).

• Deep expertise in at least one major cloud platform with cross-cloud awareness.

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

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

• Expertise in 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 business objectives into 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., The successful candidate(s) must be willing and able to perform the physical requirements of the job with or without a reasonable accommodation.

We provide our team members and their families with paid time off; 401(k) plans; affordable health, life, dental, vision and prescription drug benefits; and more.