Senior Data Engineer - Teradata/Databricks & AI/ML

Ingest and validate 18+ months of Teradata DBQL logs including SQL text, object usage, timestamps, user/application IDs, row counts, and steps.
Integrate metadata from Autosys (scheduling), DataStage (orchestration), and MagicWand (observability) to supplement DBQL analysis.
Build end-to-end, log-driven analysis pipelines in Databricks to identify unused datasets, read-only (non-updating) datasets, and unused partitions within active datasets.
Capture and analyze CPU/IO resource usage and workload statistics (ResUsage) to quantify cost-reduction opportunities.
Classify data into cold, warm, and hot tiers; generate heatmaps of date/partition access patterns.
Develop a prioritized recommendation backlog with expected savings, risk levels, and required changes.
Apply AI/ML models or LLM-assisted analysis to detect access pattern anomalies, predict cold data candidates, and automate classification.
Produce and present deliverables: Observation Report, Workshop Notes & Action Log, and Final Readout for customer stakeholders.