Ralph Hage

Management of a team of 3 technical profiles (lead software, backend algorithm
engineerand full stackdeveloper).
Served as the internal owner and administrator of the Databricks platform,
managing workspace configuration, Unity Catalog governance, role-based access
controls and group-based identity management across 5-10 production pipelines
relied upon by 3 cross-functional teams (Analytics, Data Science and BI) for day-to
day operations.
Established data governance practices from the ground up across operational
pipelines, defining core business metrics spanning solar energy production, sales
performance and operational efficiency. Introduced quality control checkpoints and
traceabilitystandards in these pipelines.
Led architecture design workshops to define target schemas, medallion-layer
specifications and consumption-layer designs for migrating heterogeneous data
sources into a unified Databricks lakehouse.
Developed 2 agentic LLM-assisted workflows for document classification across a
1TB SharePoint, using both LangGraph and AgentBricks frameworks, reducing
operational billing time by60%.

Designed, built and operated a data management and analytics system integrating
6 heterogeneous data sources (geospatial, weather, energy) into a unified
Databricks lakehouse, implementing end-to-end Bronze/Silver/Gold
transformations covering ingestion, cleansing, enrichment, qualityvalidation and
business-readyaggregationson Apache Spark.
Processed and structured over50GB of unstructured operational documents (PDF,
Word, Excel) into standardised, reusable datasets through AI agenticworkflows
and RAG for automated extraction and classification.
Executed migration and consolidation of legacyand disparate source systems into
the lakehouse architecture, includingextraction planning, mappingvalidation,
parallel runs and data integrityverification toensure reportingcontinuity post
migration.
Communicated with non-technical stakeholders to translateoperational needs into
data tools, ensuringoutputswere usable, relevant and directlyaligned with real
decision-making processes.
Supported coordination between technical and product teams to iteratively refine
2 tools and 1 automated workflow, contributing tocontinuous improvement.

Managed and structured end-to-end data workflows for several experiments,
developing documented processes for data collection, processing, analysis and
reporting, and ensuring compliance with project specifications, quality standards
and reproducibility requirements.
Produced detailed technical documentation of 5 experimental setups and
published 5 peer-reviewed publications, synthesising complex data into structured
knowledge products.
Supervised and trained 3 junior staff on data management best practices. in
analysis, modelling and automated experimentation, providing continuous technical
guidance and methodological support.