200+ institutional clients · 22 countries£100B+ HS2 programme analytics500K+ rows reconciled / month20+ hrs saved / month50%+ manual errors eliminated60% less manual processing30% fewer data defects99% data accuracy · up from 92%929 firms · thesis graded 1.0400+ compliance PDFs automated / month2 days saved per approval cycle33% faster screening cycles28% faster reporting turnaround10+ derivatives product categoriespublished on eurex.com · monthly market intelligence6 certifications · Databricks · Fabric · Power BI

Portfolio · Case Studies · Projects

I build business intelligence, automation, and analytics tools for people who need answers, not exports.

BI Analyst in financial-market analytics at Deutsche Börse Group, with earlier experience in infrastructure and consulting. I build self-service BI, Python automation, and analytical tools that turn messy data into decisions teams can defend.

Frankfurt am Main, Germany · Available from July 2026

Residence permit with full work authorization

Amit Kumar

Amit Kumar

Business Intelligence Analyst

Frankfurt am Main

Profile Snapshot

$

0+

Institutional clients

Self-service dashboards adopted across multiple stakeholders

£0B+

Programme scale

KPI analytics on HS2, the UK national rail programme

0K+

Rows reconciled / month

Monthly reconciliation with 30% fewer data defects

0+ hrs

Saved every month

Workflow automation cutting manual errors by 50%+

0+

PDFs verified monthly

Computer-vision compliance tool built in Python and OpenCV

0

Firms analyzed

Global equity event study: Thesis graded 1.0

0

Certifications

Databricks, Fabric DP-600, PL-300, Azure, AB-730, Bloomberg, McKinsey

Monthly

Published reports

Market intelligence published on the Eurex website

Proof over keywords

Skills shown through real analytics work

This portfolio is not a copy of my resume. It shows how I approach dashboards, automation, data quality, research and business reporting.

Business Intelligence

Built self-service Power BI dashboards for 200+ clearing members across 22 countries.

Automation

Automated high-volume reconciliation and reporting workflows, saving 20+ hours per month.

Python Analytics

Developed statistical outlier detection and document verification workflows using Python.

Applied Research

Completed a 1.0-graded event-study thesis analyzing 929 firms across global equity markets.

Power BIDAXPower QuerySQLPythonPandasNumPySciPyStatsmodelsRDatabricksMicrosoft FabricAzureTableauExcel VBAPower AutomateOpenCVPyMuPDF

Industry case studies

Real analytics problems solved across BI, automation and operations

These case studies focus on problems, methods and outcomes rather than repeating job descriptions.

Case Study 01

Enterprise BI & Market Intelligence Dashboards

Eurex Clearing AG · Deutsche Börse Group

Jan 2026 - Present

Power BIDAXDatabricksKPI Frameworks

Problem

Commercial and product teams needed consistent, scalable reporting across clearing members, derivatives products and liquidity trends. Static Excel reports created recurring ad-hoc requests and fragmented performance views.

Solution

Built self-service Power BI dashboards and KPI frameworks for derivatives performance across 10+ product categories. Supported the migration from static reporting into a more scalable BI setup using centralized data models.

Outcome

200+ institutional clients covered

22 countries represented

Reduced ad-hoc reporting dependency

Market intelligence used by stakeholders

Case Study 02

Statistical Outlier Detection for Institutional Fee Analysis

Eurex Clearing AG · Deutsche Börse Group

Jan 2026 - Present

PythonSciPyStatsmodelsHDFS

Problem

Institutional fee movements required faster anomaly detection and clearer visibility into the drivers behind unusual changes.

Solution

Developed a Python statistical outlier-detection model (SciPy, Statsmodels) that surfaces anomalous fee patterns and supports driver analysis across the institutional client base.

Outcome

200+ institutional clients covered

Automated anomaly detection

Reduced manual investigation effort

Faster, more consistent flagging

Case Study 03

High-Volume Reporting Automation

Deutsche Börse AG

Jul 2025 - Dec 2025

Power AutomatePower QueryVBAAI Builder

Problem

Cash market operations involved recurring manual reconciliation, file processing and reporting workflows. These tasks created manual effort, inconsistent quality checks and avoidable operational risk.

Solution

Built automation workflows with Power Automate and AI Builder, reconciled large datasets with Power Query and VBA, and created repeatable controls for recurring operational reporting.

Outcome

500K+ rows reconciled per month

20+ hours saved per month

50%+ manual error reduction

30% reduction in data defects

Case Study 04

Computer Vision Document Verification

Deutsche Börse AG

Jul 2025 - Dec 2025

PythonOpenCVPyMuPDF

Problem

Compliance-related PDFs required repetitive manual checks for stamps, signatures and exceptions. Manual review was time-consuming and exposed the process to visual fatigue errors.

Solution

Developed a Python and OpenCV-based verification tool to process compliance PDFs and flag exceptions for manual review.

Outcome

400+ PDFs processed monthly

Automated exception flagging

Reduced manual review effort

Improved consistency of compliance checks

Case Study 05

Infrastructure KPI Analytics for UK HS2 Programme

Arcadis

Aug 2022 - Jul 2024

Power BIPower AppsPythonVBA

Problem

Large infrastructure teams needed reliable KPI visibility and resource tracking across multiple work packages in the UK HS2 rail programme.

Solution

Built Power BI dashboards, Power Apps solutions and automated data transformation pipelines for engineering and project management stakeholders.

Outcome

15+ work packages supported

60% reduction in manual processing

2 days saved per approval cycle

Improved visibility for stakeholders

Independent projects

Projects built outside work to demonstrate technical execution

These projects show dashboard design, pipeline thinking, data cleaning and analytics workflow development.

Business Intelligence

Interactive BI Analytics Dashboard

Problem

Business teams often need quick, interactive reporting without waiting for static Excel exports or manual analysis.

Solution

Built a professional BI dashboard with filtering, period comparison, multiple views and export functionality.

Interactive filters by date, region, product, segment and channel

Overview, product, customer and detailed-data analysis tabs

Period-over-period comparison

CSV and Excel export options

StreamlitPandasPlotlyNumPy
View GitHub

Data Engineering

End-to-End Analytics Pipeline

Problem

Analytics workflows need reliable ingestion, transformation, validation and output layers to avoid messy manual processing.

Solution

Built a modular ETL pipeline with ingestion, cleaning, validation, aggregation and multi-format exports.

Processes 10,000+ transaction records

Schema validation and structured logging

Data quality checks for completeness and consistency

CSV, Excel and JSON outputs

PythonPandasNumPyOpenPyXL
View GitHub

Data Quality

Data Cleaning and Preparation Workflow

Problem

Raw business data often contains inconsistent dates, missing values, duplicate records and irregular formats.

Solution

Built a cleaning workflow that standardizes dates, handles missing values, removes duplicates and prepares data for BI reporting.

Date standardization

Missing-value handling

Duplicate detection

Monthly aggregation for reporting

PythonPandasData Quality
View GitHub

Applied Analytics

Data-Driven Portfolio Analysis

Problem

Investors need a practical way to understand how risk, return, and correlation interact across a multi-asset portfolio.

Solution

Built a Python analysis that measures annualized return and volatility, studies correlation, and simulates 20,000 portfolios to approximate the efficient frontier.

Risk-return measurement across multiple assets

Correlation matrix for diversification analysis

Monte Carlo efficient frontier simulation

Max-Sharpe and min-variance portfolio comparison

PythonpandasNumPymatplotlibyfinance
View GitHub

Research

Master’s Thesis: Climate Policy Reversal and Global Equity Markets

A cross-country firm-level event study of US environmental deregulation and global equity-market reactions.

Policy shocks, equity markets and event-study modelling.

Do major US climate policy reversal events generate measurable abnormal returns in global equity markets, and do firm-level characteristics or regional exposure explain differences in market reaction?

1.0

Thesis grade

929 firms

Firm sample

R + Python

Implementation

Dataset

929 firms across S&P 500, STOXX Europe 600, Nikkei 225, Hang Seng and KOSPI 200.

Method

The study estimates market-model cumulative abnormal returns and uses panel regression to compare equity return and risk responses across regions, sectors and firm characteristics.

Key takeaway

The thesis shows how policy shocks can be translated into measurable equity-market reactions using event-study methodology and panel regression.

Why it matters

The project demonstrates financial-market reasoning, data collection, event-study methodology, regression modelling and the ability to convert policy shocks into measurable market signals.

Firm-level event study covering 929 companies across the US, Europe and AsiaMarket-model CAR estimation across event windowsPanel regression implemented in R and PythonThesis graded 1.0 at Frankfurt School of Finance & Management

Insights

Writing that shows how I reason

Short analytical pieces on markets, uncertainty, and evidence-based thinking.

Markets & Economics

Does an Inverted Yield Curve Really Predict Recessions?

A reasoning-led analysis of one of the most cited recession indicators, and what the evidence actually supports.

Read analysis →

Climate Policy & Markets

When Policy Reverses, Do Markets Care?

A market analysis examining how equity markets respond to major climate-policy reversal events.

Forthcoming in peer-reviewed journal (ZfU)

Experience

Where I have applied this work

A concise timeline for context. The detailed proof of work is shown through the case studies and projects above.

Jan 2026 - Present

Eurex Clearing AG

Business Analytics Intern, Market & Liquidity Analytics

Eschborn, Germany

Jul 2025 - Dec 2025

Deutsche Börse AG

Data Analytics Working Student, Cash Market Services

Eschborn, Germany

Aug 2022 - Jul 2024

Arcadis

Data & Analytics Consultant, BIM Analytics

Bangalore, India

Feb 2022 - Jul 2022

Alliant Advisory

Associate, Cost Segregation

Bangalore, India

Jan 2021 - Jan 2022

Atal Incubation Centre - Bihar Vidyapith

Program Associate Intern, Entrepreneurship Cell

Patna, India

Education

Academic background

A concise academic overview, with thesis details shown separately in the research section.

2024 - 2026

Frankfurt School of Finance & Management

M.Sc. in Management · Frankfurt, Germany

Relevant coursework: Managerial Data Science, Financial Analysis & Performance Management, AI & Operations Decisions, Information Systems

2017 - 2021

Bangalore Institute of Technology

B.E. Civil Engineering · Bangalore, India

Relevant coursework: Engineering Mathematics, Operations Research, Project Management

Certifications

Credentials supporting BI, cloud, data engineering and markets

Certifications support the project work; they are not the main story.

Experience summary

How the work connects across industries

The goal is not to stay locked into one industry, but to apply analytics, automation and BI thinking wherever teams need better decisions.

Current focus

Business analytics, BI dashboards, market intelligence and data models at Deutsche Börse Group.

Previous analytics work

Automation, reconciliation, document verification and real-time reporting for cash market operations.

Consulting background

Infrastructure analytics, executive reporting and KPI dashboards for large engineering programmes.

Contact

Looking for Data, BI and analytics roles in Germany.

I am especially interested in finance and fintech, but open to any industry where analytics, automation and BI can create measurable business value.