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 — crafting scalable models using dimensional, multi-dimensional, and advanced normalization techniques, with enterprise-wide documentation and metadata governance.
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 build-vs-buy evaluations for data and AI tools, considering TCO, vendor lock-in, scalability, and organizational fit; manage vendor relationships.
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.
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.
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
Expertise in modern data platforms (Databricks, Snowflake, BigQuery), lakehouse architectures (Delta Lake, Iceberg), and streaming (Kafka, Flink, Pub/Sub).
Mastery of orchestration, transformation (dbt), containerization (Docker, K8s), and IaC (Terraform).
Expertise in CI/CD, data observability, governance, data mesh, and platform reliability.
Expert-level knowledge of agentic AI architectures, LLMOps, RAG, knowledge graphs, and AI governance/safety/security.
Soft Skills
**Not eligible for visa sponsorship **
** Not eligible for relocation assistance **
Relocation Assistance Eligible:
No
Work Shift:
1ST SHIFT (United States of America)
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