Turn STTMs and Legacy ETL Metadata into Governed Data-Engineering Deliverables
DE Copilot converts business requirements, source-to-target mappings and legacy ETL metadata into validated, reviewable SQL, DDL, data-quality rules, technical specifications, lineage and delivery packages - all through a governed Canonical Metadata Model.
The prototype is hosted on Streamlit and may take a moment to restart after a period of inactivity. The prototype is hosted on Streamlit and may take a moment to restart after a period of inactivity.
Start from business intent or legacy implementation.
Canonical Metadata Model
Normalized metadata layer
AI Intelligence Layer
DE Copilot Processing Engine
Generated Artifacts
Human Review & Approval
Engineers stay in control
Deployment
Reviewable artifacts, approved export
Observability & Audit Trail
Lineage, quality, governance
Metadata Intelligence
Transform enterprise knowledge and metadata into actionable engineering assets using AI.
Human-in-the-Loop Governance
AI generates recommendations while engineers review, approve, and maintain control.
Observability & Auditability
Track lineage, monitor data quality, explain generated artifacts, and maintain a complete audit trail.
Built by a Lead Data Engineer with 15+ years of enterprise data platform experience across Snowflake, Databricks, Spark, Metadata Management, Data Quality, and AI-Assisted Engineering.
15+
Years Enterprise Data Engineering
15
Technical Articles Published
3+
Judging Engagements
3
Conference CFP Submissions
10+
Technical Resources
1
Founder of DE Copilot
Judging, Speaking & Writing Across the Community
Active contributions to the AI, data engineering, and developer community through technical evaluation, conference speaking, and published writing.
Technical Judge
AI · Cybersecurity · Software Engineering
MLH / DEV
4+ challenges · 2026
LabLab.ai
2+ hackathons · 2026
SANS Institute
AI Cybersecurity · 2026
Devpost
AI Challenge · 2026
Conference CFPs
Submitted · 2026
PyBay 2026
SubmittedMetadata-Driven Data Engineering with Python
DevFest KC
SubmittedEnterprise AI Architecture for Data Engineering
AI Dev World
SubmittedBuilding AI Copilots for Enterprise Data Engineering
Technical Author
15 articles · Book chapter accepted
Chapter Accepted - Nov 2026
"Metadata Intelligence for Next-Generation Big Data"
Next-Generation Big Data Vol. 6B · IIP Series
What's New
Start From Business Intent or Legacy Implementation
Data delivery can begin with a business requirement, a mapping specification, or an existing legacy implementation. DE Copilot brings each input into one governed Canonical Metadata Model before artifacts are generated and released.
Path 1
Business Requirement / Mapping Specification
Upload business requirements, Source-to-Target Mappings, spreadsheets, or technical documents. Extract definitions, business rules, mappings, data types, and assumptions into reviewable engineering metadata.
Path 2
Legacy ETL Modernization
Upload legacy ETL mapping metadata, beginning with Informatica-style mappings. Extract sources, targets, transformations, expressions, lookups, connectors, lineage, and migration risks before generating modern data-engineering artifacts.
Shared Governed Delivery Workflow
Your team is too good for repetitive work
Most data engineering teams spend the majority of their time on tasks that are predictable, templated, and ripe for automation.
Legacy ETL Modernization
Legacy ETL migration is more than code conversion. DE Copilot helps teams extract transformation intent from mapping metadata, identify unsupported or risky logic, generate Snowflake-ready delivery artifacts, and require review before export.
Initial support focuses on source and target metadata, expressions, mappings, connectors, and transformation inventory. Complex lookup, update-strategy, and custom transformation logic is explicitly routed for engineering review.
What Gets Extracted
What DE Copilot Does Today - and Where It's Heading
A practitioner-built metadata-intelligence and delivery accelerator. Capabilities are delivered incrementally and clearly labeled by status.
Available Now
In Development
Initial Informatica support focuses on source and target metadata, expressions, mappings, connectors, and transformation inventory.
Planned
Planned capabilities are not yet available. This list reflects the intended direction, not current functionality.
See What It Produces
Given a sample Customer 360 or retail STTM, DE Copilot generates the following artifacts. All examples are synthetic.
Introducing SAGE: Semantic AI Governed Engineering
SAGE is a practitioner-oriented framework for applying AI to enterprise data engineering without losing governance, traceability or human accountability. It combines governed metadata, enterprise knowledge, AI-assisted generation, automated validation, human approval and continuous learning within one engineering workflow.
Metadata becomes the source of engineering truth. AI becomes the accelerator. Human expertise remains the final authority.
From Metadata to Governed Data Delivery
DE Copilot turns business requirements, mappings, and legacy ETL metadata into validated, reviewable engineering artifacts with approval history, assumptions, risk findings, and delivery observability.
01
Project Workspace
02
Metadata Validation
03
Artifact Generation
04
Human Review
05
Audit & Observability
Project Summary
Project
Retail Analytics Modernization
Workspace
Demo Workspace
Environment
Sandbox
Metadata Records
128 Columns
Last Run
Today, 10:30 AM
Validation Status
Generated Assets
Governance & Control
3
Review Queue
Pending
5
Assumptions
Captured
18
Audit Events
Logged
1
Open Incidents
Active
1.8 hrs
Avg Resolution
MTTR
86%
Delivery Status
Ready
Approval Timeline
Generated
Reviewed
Approved
Exported
Illustrative demo workspace using fictional metadata. No client, employer, or production data is displayed.
Legacy Migration Workspace: Informatica Mapping → Snowflake Delivery Packet in active development.
Metadata Validation
Validate mappings, data types, missing rules, duplicates, and source-to-target completeness before generation begins.
Artifact Generation
Generate DDL, SQL, data quality controls, reconciliation rules, documentation, and technical specifications from governed metadata.
Human Review & Audit
Route outputs through review, capture assumptions, preserve approval history, and maintain traceability across every generated artifact.
Observability & MTTR
Track generation runs, validation failures, open issues, resolution time, and delivery readiness in one unified workspace.
Capabilities Available and In Active Development
Core governed delivery capabilities available in the live prototype today, plus what is actively being built.
Evolving into an Enterprise Metadata Intelligence Platform
DE Copilot is evolving from a metadata-to-artifact generator into a full Enterprise Metadata Intelligence Platform supporting AI-assisted engineering, metadata automation, governance, documentation, testing, and deployment across the entire data engineering lifecycle.
The next phase of DE Copilot focuses on a simple principle:
The platform will continue to strengthen data engineering delivery while expanding into reusable, governed AI solutions for data-intensive enterprise workflows.
1. Data Engineering Intelligence
A governed workflow for turning business requirements, STTM, source metadata, SQL, DDL, and legacy ETL assets into validated, reviewable, and release-ready engineering deliverables.
Every mapping suggestion, join recommendation, DQ rule, migration risk, and validation finding will show its supporting metadata, source evidence, or business rule.
Generated outputs will be clearly marked as Confirmed, Metadata-Supported, Inferred, Needs SME Validation, or Blocked.
Artifacts will move through validation, engineer review, SME approval, architect signoff, versioned re-review cycles, and export or release decisions.
Detect missing mappings, undocumented joins, datatype mismatches, unclear grain, missing business keys, incomplete derivation logic, PII gaps, and missing DQ coverage.
Generate row-count checks, financial reconciliations, duplicate checks, null checks, referential-integrity rules, negative tests, boundary tests, and incremental-load validation.
Expand from Informatica XML toward additional legacy assets such as DataStage, SSIS, Talend, stored procedures, dbt projects, and Spark or Databricks metadata.
Show how source schema, business-rule, or mapping changes affect target tables, SQL, DQ rules, tests, dashboards, semantic models, and downstream data products.
Capture mapping rationale, architecture decisions, known risks, approval history, reviewer feedback, source-system assumptions, and lessons learned.
2. Custom Governed AI Solutions
DE Copilot will apply the same evidence, confidence, review, and audit model to enterprise workflows that need trusted AI assistance beyond engineering artifact generation.
Investigate data variances, identify affected sources and pipelines, surface supporting evidence, propose next checks, and create reviewable investigation summaries.
Connect pipeline failures, schema changes, DQ alerts, recent deployments, lineage, ownership, and runbooks into an evidence-backed support workflow.
Answer governed questions using approved metadata, lineage, definitions, ownership, DQ status, technical specifications, and project decisions.
Analyze legacy ETL assets, extract transformation logic, identify complexity and migration risk, and generate modernization work packets.
Show whether a data product is ready for release based on mapping completeness, validation results, DQ coverage, lineage, test evidence, approvals, open risks, and operational readiness.
Example Questions Metadata Knowledge Copilot
- ›What is the authoritative source for this attribute?
- ›Why is this field derived?
- ›Which targets and reports depend on this source column?
- ›What DQ rules protect this metric?
- ›Who approved the latest mapping change?
Product Principles
Capabilities are delivered incrementally. Current Snowflake-focused workflows are available in the live prototype. Additional platforms, integrations, legacy adapters, and custom AI solution modules are planned or in development.
Governed Metadata. Reviewable Delivery.
DE Copilot helps data teams transform structured mappings and definitions into validated, reviewable engineering deliverables with human approval, audit history, and delivery visibility built in.
DE Copilot is built around a controlled delivery workflow:
DE Copilot does not treat generated code as automatically production-ready. It validates the underlying metadata, identifies missing information and migration risks, captures reviewer decisions, and supports approved export only after human signoff.
Validate Before You Generate
Check metadata completeness, data types, duplicate mappings, and missing business rules before creating artifacts.
Keep Humans in Control
Route generated outputs through review and approval workflows, while capturing assumptions and decisions.
Deliver with Traceability
Maintain audit history, run visibility, issue tracking, and a clear path to approved exports.
Generate Faster. Release Safely.
Generated SQL, DDL, mappings, data-quality rules, technical specifications, and migration artifacts should not move directly into delivery. The Copilot Release Gate checks metadata completeness, datatype compatibility, mapping coverage, unresolved assumptions, and risk findings before artifacts are approved for export.
Validation Findings
Identify missing rules, invalid mappings, datatype conflicts, duplicates, and incomplete lineage.
Migration Risk Assessment
Flag unsupported legacy transformations, lookup conversion decisions, schema mismatches, and manual-review requirements.
Assumptions Register
Capture defaults, business-rule gaps, design choices, exceptions, and unresolved decisions.
Human Approval
Allow engineers, SMEs, and architects to approve, approve with conditions, request changes, or block export.
Artifact Release Status
Real outcomes for data teams
Built by a practitioner who has lived these problems across insurance, banking, and retail data platforms.
share of sprint time spent on repetitive, automatable tasks
path from governed mapping to deployable engineering artifacts
onboarding through surfaced project knowledge and delivery artifacts
engineering artifacts ready for validation and approval before release
Read More About DE Copilot
Technical articles on metadata-driven engineering, ETL modernization, and governed data delivery.
Built from Real Enterprise Engineering Experience
DE Copilot was not designed from theoretical examples. Every capability reflects a real problem encountered across 15+ years of enterprise data engineering programs.
Designed from Enterprise Reality
The problems DE Copilot solves manual STTM translation, undocumented assumptions, ungoverned artifact generation, and slow onboarding are problems the founder has lived across insurance, retail, financial services, and e-commerce data programs.
Governance is Not an Afterthought
Human review, approval workflows, assumptions registers, and audit trails are core to the platform architecture not optional add-ons. DE Copilot treats governance as a first-class engineering requirement.
AI Assists. Engineers Decide.
Every generated artifact is routed through a review and approval workflow. DE Copilot surfaces recommendations, flags risks, and captures decisions but engineers remain in control of what gets released.
Traceability from Metadata to Delivery
The Canonical Metadata Model ensures every generated artifact DDL, SQL, DQ rules, documentation can be traced back to its source metadata, business rule, and approval decision.
Platform Extensibility by Design
DE Copilot currently provides Snowflake-focused generation workflows. Its Canonical Metadata Model is designed to support future expansion into Databricks, PySpark, dbt and additional engineering platforms.
Built for Engineering Teams, Not Just Analysts
DE Copilot generates review-ready artifacts - DDL, transformation SQL, data quality rules, and technical specifications - designed to be reviewed and deployed by engineers.
Amit Singh Founder, Data Engineering Copilot
Enterprise Data Engineering Leader · Enterprise AI Architect · Technical Author · Hackathon Judge · Speaker
"I built DE Copilot because I kept solving the same problem across every enterprise data program I worked on translating the same business metadata into different technical formats, manually, with no reusable layer underneath. The platform is my answer to that problem."
Full professional profileRecognized by the Community
Thanks to our judges including ... AMIT SINGH ... and many more.
Rob T. Lee
Chief AI Officer and Chief of Research, SANS Institute - Find Evil! AI Hackathon
3+
Engagements
5
Organizations
4
Domains
Verified judge certificates
AMD Developer Hackathon: Act IIFind Evil! AI Cybersecurity ChallengeBand of Agents HackathonRequest Access, Demo, or Collaboration
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Speaking Invitation
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Judging Inquiry
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DE Copilot is a personal product prototype focused on metadata-driven data engineering. All examples, mappings, screenshots, datasets, and sample artifacts are synthetic demonstration material. No employer, client, internal platform, proprietary information, or production data is used.