Ioannis - Personal Portfolio

I'm Ioannis Tsarmantidis

I'm a Freelancer.

I'm a SAP Consultant.

I'm a Data Analyst.

Welcome

based in Leipzig, Saxony, Germany.

About Me

Know Me More

I'm Ioannis, a SAP Data Migration Consultant and Data Analyst

I help companies migrate data smoothly and securely by managing the entire process end-to-end. From extracting and analyzing raw legacy data with Python-based tools to automatically filling SAP templates, cleansing inconsistencies, and ensuring everything is ready for go-live – I handle it all with precision and speed.

My work doesn’t stop at the load. I also deliver executive-level reporting, verify the integrity of migrated data through smart reconciliation, and run post-migration tests to ensure everything transferred is complete, accurate, and audit-proof.

Name:Ioannis Tsarmantidis
Email:Ioannis@Tsarmantidis.com
Age:27
From:Leipzig, Saxony, Germany

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4+

Years Experiance

4

Projects migrated

9+

Knowledge in key SAP functional areas

48+

Knowledge in SAP Migration Objects

Services

What I Do?

SAP S/4HANA Cloud Migration

End-to-end data migration including extraction, transformation and reconciliation using SAP Migration Cockpit, HANA SQL and staging tables.

Data Profiling & Cleansing

Automated data quality checks and transformation pipelines using Python (pandas) and Alteryx to ensure audit-ready migration datasets.

Python Development

Scripting for ETL workflows, reconciliation logic, and analytics dashboards – including automation of Excel reports and SAP uploads.

Alteryx Automation

Workflow design and deployment for rule-based cleansing, delta tracking and project-wide data transformations at scale.

Power BI Reporting

Dynamic KPI dashboards and validation reports to support business and audit stakeholders during cut-over and hypercare.

ITGC & Audit Readiness

Support for ITGC compliance, documentation and evidence generation aligned with IDW PS 880 and ISAE 3000/3402 standards.

SAP Migration & Analytics

Process Flow of Data Analysis and SAP Migration

Part 1

Data Intake

Bringing legacy data onto the starting line for SAP S/4HANA

1.1 Agreement & Governance

A kick‑off workshop with the client captures company structure, booking circles, target countries, migration objects and key contacts. The outcome is an Audit‑Conform Data Charter that defines privacy rules, retention periods and assurance standards (SOX ITGC, IDW PS 330, ISAE 3402). All subsequent steps trace back to this charter.

1.2 Data Extraction

The legacy landscape delivers data in the format agreed during scoping — flat files, database exports or API feeds. A unified Python workflow ingests every file, preserves structural metadata and applies a checksum so lineage is never lost.

1.3 Volume‑Driven Split

Volume Band	Migration Path	Tool Stack
< 200 GB	XML / CSV templates via SAP Migration Cockpit	Python · pandas · XML generator
≥ 200 GB	SAP HANA staging tables with package splitter	SQL · pandas · checksum routine

Both branches share naming conventions and checksum logic, so downstream handling stays identical.

1.4 Periodic Deltas & Final Snapshot

Scheduled delta extractions keep the dataset current throughout the project. Just before cut‑over a final full pull is taken, giving the team a definitive legacy snapshot for one‑to‑one reconciliation against S/4HANA master data.

1.5 Ready for Profiling

With secure, lineage‑tracked datasets in place the pipeline moves into Automated Profiling & Cleansing — covered in Part 2.

Part 2

Data Profiling & Cleansing

Automated quality checks and quick‑win fixes aligned to SAP S/4HANA requirements

2.1 Profiling Engine (Python + Alteryx)

A combined Python / Pandas / NumPy stack scans every extract for completeness, uniqueness, pattern conformity and statistical outliers. Alteryx workflows plug into the same pipeline for rapid data prep when visual drag‑and‑drop is faster than code. All checks reference the S/4HANA migration object templates, ensuring early compliance with field lengths, data types and key rules.

2.2 Cleansing & Rule Feedback

Cleansing rules are autogenerated from profiling findings: missing tax codes, malformed IBANs, duplicate business partners. Fixed records flow straight back into the XML or staging branch using the same naming conventions. Findings are logged and discussed in Data Quality Calls with Finance, Logistics and Sales representatives.

2.3 Live Quality Dashboard

A Power BI report surfaces Data‑Quality‑Scores (0–100) per migration object and highlights critical fields. The dashboard refreshes automatically from the Python pipeline and anchors every cleansing workshop with up‑to‑date evidence.

2.4 Ready for Mapping

With key issues resolved and quality scores in the green zone, the project moves into Field & Value Mapping — covered in Part 3.

Part 3

Field & Value Mapping

Aligning legacy structures to S/4HANA target shapes before simulation

3.1 Legacy → S/4 Field Mapping

Source fields from Finance, Logistics and Sales are mapped to their S/4HANA Cloud counterparts in a structured mapping sheet. Each column carries business definitions so work‑streams can validate meaning, not just names. First‑pass templates are uploaded to the Migration Cockpit to verify mandatory fields and data types.

3.2 Work‑Stream Value Mapping

Functional leads review legacy codes and decide how they translate into the new configuration — for example merging booking circles, introducing new profit centre hierarchies or adjusting customer account groups. The final mapping tables become living reference docs for later cleansing and simulation loops.

3.3 Unit, Currency & Time‑Zone Harmonisation

Reference tables convert units (EA → ST), currencies (legacy → EUR) and timestamps into the client’s standard time‑zone. These tables are version‑controlled and reused in every load cycle so results remain reproducible.

3.4 Ready for Simulation

With field and value mappings locked, the project moves into the Simulation ⇄ Error Loop — covered in Part 4.

Part 4

Simulation ⇄ Error Loop

Iterative uploads that turn mapping theory into migration reality

4.1 Template Simulation

The Simulation flag in SAP Migration Cockpit validates templates and staging tables without creating postings. Mandatory-field violations, data‑type mismatches and foreign‑key gaps surface early — long before they can threaten the cut‑over timeline.

4.2 Smoke Test

A small, representative slice of data — typically one company code and a handful of materials or customers — is migrated end‑to‑end. Business users check the result in Fiori apps and sign off that master‑data look‑ups, postings and authorisations behave as expected.

4.3 Error Harvesting

Python scripts read SAP Migration Cockpit error classes such as “mandatory field”, “datatype” or “missing key” are automatically grouped and logged, giving work‑streams a concise view without sifting through thousands of individual lines.

4.4 Correction & Re‑Run

Mapping tables and source values are adjusted; the next simulation or batch run confirms the fix. The loop repeats until work‑streams confirm that remaining messages are informational only.

4.5 Ready for Progressive Load

With errors under control and users satisfied, the project advances to Progressive Load & Reconciliation — covered in Part 5.

Part 5

Progressive Load & Reconciliation

Controlled waves of data migration

5.1 Batch Migration

Data moves in scheduled waves — typically by company code or fiscal year — so business teams can validate results incrementally. Each wave closes only after functional sign‑off, reducing cut‑over risk and easing hypercare.

5.2 Tolerance Gates

After each wave, key figures — open AR/AP, stock quantities, asset values, cost‑centre balances — are compared between legacy and S/4HANA. Accept/reject thresholds are agreed upfront with Finance and Controlling leads.

5.3 Variance Handling

Any deviation outside the tolerance gate triggers either a quick data fix followed by re‑load, or a rollback to the previous wave. Decision authority and rollback scripts are defined in the Cut‑Over Plan, keeping accountability crystal‑clear.

5.4 Ready for Audit Package

With all waves reconciled and signed off, the team proceeds to Audit Trail & Documentation — Part 6.

Part 6

Audit Trail & Documentation

Creating a review‑ready evidence package for external assurance

6.1 Evidence Compilation

Runbooks, mapping sheets, reconciliation logs and approval e‑mails are consolidated into a single Audit Evidence Package. Each document cross‑referenced so reviewers can trace every data point back to its source.

6.2 Compliance Alignment

The documentation package is audit-ready, complete, and structured to align with established best practices for governance and compliance. Control frameworks are transparently linked to supporting evidence, enabling efficient and reliable external reviews.

6.3 Handover & Lessons Learned

The evidence package and methodology doc are handed over to the client. Proprietary Python and Alteryx tooling remain in‑house, but a Lessons‑Learned Log captures optimisation ideas for future roll‑outs.

6.4 Project Closure

With audits passed and stakeholders satisfied, the migration project formally closes and transitions into hypercare support.

Part 7

Dashboards & Stakeholder Enablement

Real‑time visibility and knowledge transfer for every project role

7.1 Power BI Dashboards

KPI tiles show Data‑Quality, reconciliation status, wave progress and open issues. The report auto‑refreshes from the Python pipeline, so every role sees the same source of truth.

7.2 Workshops & Clinics

Regular Data‑Quality Clinics, Cut‑Over Readiness Sessions and ad‑hoc issue triage keep Finance, Logistics and Sales aligned. Slide decks pull live visuals from the dashboard, eliminating manual copy‑and‑paste.

7.3 Stakeholder Materials

Quick‑reference guides and FAQ sheets explain how to read dashboards, approve waves and raise defects — boosting self‑service and reducing project noise.

Part 8

Lessons Learned & Continual Improvement

Feeding project insights into the next rollout wave

8.1 Lessons‑Learned Log

A structured log records what worked, what didn’t and why — from compression settings to workshop formats. Entries are tagged so future rollouts can filter for relevant topics.

8.2 Methodology Refinement

Findings feed back into mapping templates, dashboard visuals and risk logs. This continuous‑improvement loop accelerates future migrations and reduces cost for the client.

8.3 Knowledge‑Base Update

Best‑practice snippets, SQL scripts and template examples (stripped of client data) enrich the internal knowledge base. This preserves know‑how while keeping proprietary tooling private.

Summary

Resume

Professional Experience

Oct 2023 – May 2025

Associate – SAP Data Migration

PwC Germany, Leipzig

Led four S/4HANA migrations end‑to‑end: Python/Alteryx profiling, template & staging loads via SAP Migration Cockpit, HANA SQL transformations and Power BI reconciliation dashboards. Crafted cut‑over runbooks and facilitated key‑user workshops.

Sep 2021 – Oct 2023

Working Student – IT Audit & Data Migration

PwC Germany, Leipzig

Supported statutory IT‑audits (ISAE 3000/3402, IDW PS 880). Prepared migration templates, executed trial loads and reconciled results with Fiori apps.

Jun 2021 – Jul 2021

Working Student – DevOps & Cyber Security

Exxeta AG, Leipzig

Worked with Docker, Kubernetes and CI/CD pipelines to harden delivery workflows.

Nov 2020 – Apr 2021

Working Student – Risk Advisory

Deloitte GmbH, Leipzig

Analysed financial datasets with JET Analytics for SOX‑compliant risk testing and produced Power BI insights for audit reports.

Key Skills

SAP Migration Cockpit 90%

Python (pandas) 85%

Excel 95%

Power BI 75%

Alteryx Designer 65%

SQL (PostgreSQL/HANA) 70%

Portfolio

My Work

Automotive

Greenfield S/4HANA migration — MM, PP, EWM.

Energy

Carve‑out with focus on FI and asset accounting.

Media

Subscription billing, SD objects, CVI conversion.

Aerospace

Complex BOM & batch migrations under strict compliance.

Coming Soon

Future client references and showcase projects will appear here once disclosed.

Data‑Science Highlights

Detailed data‑science case studies will be added once public references are available.