Turn Business Requirements and Legacy ETL Metadata into Governed Data Delivery
DE Copilot transforms Source-to-Target Mappings, business requirements, spreadsheets, and legacy ETL metadata into validated, reviewable engineering artifacts. Generate Snowflake-ready SQL, DDL, data dictionaries, technical specifications, lineage, data-quality controls, migration-risk findings, and approval-ready delivery packets.
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.
Hi, I'm Amit Singh.
I've spent 15+ years helping enterprises modernize data platforms across insurance, retail, financial services, and e-commerce — at companies including Walmart, EXL, Cognizant, IBM, and L&T Infotech.
I built DE Copilot because I kept seeing the same problem on every engagement: engineers spending weeks manually translating metadata into SQL, documentation, data quality rules, and technical specifications. The same work, repeated, on every project, with no reusable layer underneath.
I believe metadata — not code — is the real asset. DE Copilot is my answer to that problem.
15+
Years Enterprise Data Engineering
12+
Technical Articles Published
7+
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
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
One copilot. Every layer of the lifecycle.
DE Copilot is designed around distinct intelligences each targeting a different layer of the data engineering lifecycle, from code generation to enterprise-scale deployment.
From mapping to working code automatically
STTM-to-Code Generation reads your source-to-target mapping documents and produces production-ready SQL and PySpark. No manual translation, no copy-paste errors.
Auto-generate SQL and PySpark directly from your mapping specs. Eliminate manual translation and reduce delivery time by days.
Auto-generate and keep technical docs in sync with your pipelines, mappings, and code without the manual overhead.
Catch issues before they reach production
AI-powered data quality and reconciliation intelligence that surfaces problems early so your pipelines ship clean data, every time.
AI-suggested data quality rules based on your schema, domain, and historical patterns. Catch issues before they reach production.
Automated reconciliation checks with intelligent discrepancy detection and root cause hints across source and target systems.
Designed to accelerate onboarding by surfacing grounded project knowledge, mappings, and delivery artifacts.
Unlock institutional knowledge buried in your data estate and help new engineers get productive faster.
Ask questions, get answers grounded in your pipelines, mappings, and architecture. Surface what your team already knows.
AI-guided walkthroughs of your data architecture, pipelines, and standards. Designed to accelerate onboarding by surfacing the right knowledge artifact at the right moment.
Designed to reduce the path from governed mapping to a deployable engineering repository.
Blueprint-Driven Repository Factory takes your STTM and your company's template repo, then scaffolds a fully wired, governance-compliant data product with code, tests, and CI/CD baked in.
Upload your company's standard repo zip. The engine injects generated PySpark/SQL into the exact source directories, preserving your logging, linting, and CI/CD standards automatically.
Auto-generates matching unit test files with mock inputs and updates Airflow DAGs or dbt schedules to register the new assets zero boilerplate overhead.
From weeks of searching to answers in seconds.
Project Intelligence Copilot is an AI-powered onboarding assistant that answers project-specific questions using your actual documentation STTMs, architecture docs, access guides, meeting notes, and more. New engineers ramp up in hours, not weeks.
Ask anything about the project. Get answers grounded in your actual STTMs, architecture documents, and access guides not generic AI guesses.
Designed to help new team members get productive faster. The copilot surfaces the right knowledge artifact at the right moment, reducing time spent searching through emails and docs.
End-to-end lineage from source to target automatically.
Enterprise Lineage Engine traces every transformation from source systems through mappings to target models, generating complete, auditable data lineage without manual documentation.
Ingest your STTMs and source system metadata to automatically produce end-to-end lineage graphs from raw source fields to final target columns.
Instantly understand the downstream impact of any schema or mapping change. Know exactly which pipelines, reports, and targets are affected before you deploy.
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-Agnostic by Design
The same governed metadata workflow supports Snowflake, Databricks, PySpark, and dbt. The platform is designed to generate artifacts for the target environment your team actually uses.
Built for Engineering Teams, Not Just Analysts
DE Copilot generates production-quality artifacts not summaries or suggestions. DDL, transformation SQL, data quality rules, and technical specifications are 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 profileAmit Singh
Founder, Data Engineering Copilot
Amit Singh is a Data Engineering leader with 15+ years of experience building enterprise data platforms across insurance, retail, financial services, and e-commerce. He is the Founder of Data Engineering Copilot, a metadata-driven platform for governed data delivery.
Amit serves as a technical judge for AI and technology competitions, is a technical author with 12 published articles, and is available for speaking at meetups, conferences, corporate events, podcasts, and webinars.
DE Copilot is built from that experience not from theoretical examples. Every capability reflects a real problem encountered across large-scale enterprise data programs.
Request Access, Demo, or Collaboration
Whether you want early access to DE Copilot, a product demo, a speaking invitation, or a judging inquiry reach out directly.
Request Early Access
Join the waitlist for DE Copilot. Be among the first teams to try the platform.
Speaking Invitation
Invite Amit to speak at your meetup, conference, corporate event, podcast, or webinar.
Judging Inquiry
Invite Amit to judge your AI or technology competition, hackathon, or innovation challenge.
Join the Early Access Waitlist
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No spam. No commitments. Just early access.
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.