This role bridges enterprise data architecture and applied AI, defining how AI/ML technologies can automate metadata discovery, lineage analysis, and data-modernization planning.
Key Responsibilities
Develop and execute an
AI/ML strategy
for automating metadata ingestion, lineage inference, and anomaly detection. Identify and evaluate opportunities to embed
Generative AI
and predictive analytics into existing data-engineering processes. Collaborate with data-engineering and architecture teams to integrate AI solutions within
AWS
and
Azure
ecosystems. Assess, prototype, and recommend tools such as
Azure AI Services ,
AWS SageMaker ,
Databricks ML , and
OpenAI APIs . Define long-term modernization and automation roadmaps; produce clear documentation and stakeholder presentations. Establish governance and model-lifecycle best practices for AI components integrated into the data platform. Required Skills & Experience 10-15 years
overall experience, with
3-5 years
in AI/ML strategy or enterprise data-modernization leadership. Proven ability to design and implement
AI-driven architectures
for data-management or metadata initiatives. Strong understanding of
metadata management ,
data-lineage frameworks , and
data-governance principles . Hands-on familiarity with
cloud AI platforms : Azure AI, AWS SageMaker, Databricks ML Flow, or comparable tools. Experience defining
AI roadmaps, proof-of-concepts, and ROI frameworks
for large organizations. Excellent stakeholder communication, executive reporting, and technical-writing skills.
Nice to Have Consulting background in
AI advisory ,
data strategy , or
digital-transformation
programs. Exposure to
GenAI use cases
for data quality, lineage inference, or ETL optimization. Understanding of
data-modernization tools
(BladeBridge, Informatica IDMC, Collibra, Alation).
Develop and execute an
AI/ML strategy
for automating metadata ingestion, lineage inference, and anomaly detection. Identify and evaluate opportunities to embed
Generative AI
and predictive analytics into existing data-engineering processes. Collaborate with data-engineering and architecture teams to integrate AI solutions within
AWS
and
Azure
ecosystems. Assess, prototype, and recommend tools such as
Azure AI Services ,
AWS SageMaker ,
Databricks ML , and
OpenAI APIs . Define long-term modernization and automation roadmaps; produce clear documentation and stakeholder presentations. Establish governance and model-lifecycle best practices for AI components integrated into the data platform. Required Skills & Experience 10-15 years
overall experience, with
3-5 years
in AI/ML strategy or enterprise data-modernization leadership. Proven ability to design and implement
AI-driven architectures
for data-management or metadata initiatives. Strong understanding of
metadata management ,
data-lineage frameworks , and
data-governance principles . Hands-on familiarity with
cloud AI platforms : Azure AI, AWS SageMaker, Databricks ML Flow, or comparable tools. Experience defining
AI roadmaps, proof-of-concepts, and ROI frameworks
for large organizations. Excellent stakeholder communication, executive reporting, and technical-writing skills.
Nice to Have Consulting background in
AI advisory ,
data strategy , or
digital-transformation
programs. Exposure to
GenAI use cases
for data quality, lineage inference, or ETL optimization. Understanding of
data-modernization tools
(BladeBridge, Informatica IDMC, Collibra, Alation).