3 godzin(y) temu -
[center]![[Obrazek: 62bb48252d4d9f9218e8292ae3e36f18.jpg]](https://i126.fastpic.org/big/2026/0103/18/62bb48252d4d9f9218e8292ae3e36f18.jpg)
Data Engineering For Risk & Finance (sql , Aws, Azure)
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 10h 23m | Size: 8 GB [/center]
Build real-world risk data pipelines using SQL, Python, AWS & Azure for banking, IFRS 9 & finance
What you'll learn
Design end-to-end data engineering pipelines for financial risk use cases (credit risk, IFRS 9, ECL, regulatory reporting).
Build production-ready SQL data models for PD, LGD, EAD, exposure, and time-series macroeconomic data.
Implement cloud-based data pipelines on AWS and Azure, including ingestion, transformation, and orchestration.
Engineer risk-grade data layers (raw, staging, curated, analytics) aligned to banking governance and audit expectations.
Integrate Python, SQL, and cloud services to automate data validation, reconciliation, and controls.
Apply data quality frameworks used in regulated financial institutions (completeness, accuracy, timeliness, lineage).
Prepare model-ready datasets for credit risk, stress testing, and IFRS 9 scenario analysis.
Translate regulatory and business requirements into scalable data architectures for risk and finance teams.
Requirements
Basic understanding of SQL (SELECT, JOIN, GROUP BY)
Familiarity with Python or any programming language is helpful
General awareness of finance, banking, or risk concepts (not required)
Description
This course contains the use of artificial intelligence.Modern banks and financial institutions do not fail because of a lack of models, they fail because of poor data engineering.This course is designed to teach you how real financial risk and finance data platforms are built, maintained, and governed in production environments using SQL, Python, AWS, and Azure.Unlike generic data engineering courses, this program is purpose-built for banking, risk, and finance use cases, including credit risk, IFRS 9, regulatory reporting, and enterprise analytics. You will learn how to design end-to-end data pipelines that transform raw, messy financial data into trusted, auditable, model-ready datasets.You'll start by mastering risk-grade SQL engineering, building structured data layers for exposures, customers, transactions, macroeconomic variables, and time-series financial data. From there, you'll integrate Python-based data processing, validation, and automation techniques used in real risk teams.The course then expands into cloud-native architectures, showing you how financial institutions use AWS and Azure to ingest, transform, store, and govern large volumes of risk and finance data. You'll learn practical patterns such as raw → staging → curated → analytics layers, reconciliation controls, data quality checks, lineage, and auditability.Throughout the course, concepts are explained step-by-step from first principles, with a strong focus on practical implementation rather than theory. Every section is aligned with regulatory expectations, making this course highly relevant for professionals working in banking, fintech, consulting, or enterprise analytics.By the end of the course, you will be able to design and explain production-ready data engineering solutions for financial risk, bridging the gap between data engineering and business-critical decision-making.
Who this course is for
Risk analysts transitioning into data engineering
Data engineers working in banking, finance, or fintech
Credit risk, IFRS 9, Basel, and regulatory professionals
SQL/Python users who want real financial use cases
Data Engineering For Risk & Finance (sql , Aws, Azure)
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 10h 23m | Size: 8 GB [/center]
Build real-world risk data pipelines using SQL, Python, AWS & Azure for banking, IFRS 9 & finance
What you'll learn
Design end-to-end data engineering pipelines for financial risk use cases (credit risk, IFRS 9, ECL, regulatory reporting).
Build production-ready SQL data models for PD, LGD, EAD, exposure, and time-series macroeconomic data.
Implement cloud-based data pipelines on AWS and Azure, including ingestion, transformation, and orchestration.
Engineer risk-grade data layers (raw, staging, curated, analytics) aligned to banking governance and audit expectations.
Integrate Python, SQL, and cloud services to automate data validation, reconciliation, and controls.
Apply data quality frameworks used in regulated financial institutions (completeness, accuracy, timeliness, lineage).
Prepare model-ready datasets for credit risk, stress testing, and IFRS 9 scenario analysis.
Translate regulatory and business requirements into scalable data architectures for risk and finance teams.
Requirements
Basic understanding of SQL (SELECT, JOIN, GROUP BY)
Familiarity with Python or any programming language is helpful
General awareness of finance, banking, or risk concepts (not required)
Description
This course contains the use of artificial intelligence.Modern banks and financial institutions do not fail because of a lack of models, they fail because of poor data engineering.This course is designed to teach you how real financial risk and finance data platforms are built, maintained, and governed in production environments using SQL, Python, AWS, and Azure.Unlike generic data engineering courses, this program is purpose-built for banking, risk, and finance use cases, including credit risk, IFRS 9, regulatory reporting, and enterprise analytics. You will learn how to design end-to-end data pipelines that transform raw, messy financial data into trusted, auditable, model-ready datasets.You'll start by mastering risk-grade SQL engineering, building structured data layers for exposures, customers, transactions, macroeconomic variables, and time-series financial data. From there, you'll integrate Python-based data processing, validation, and automation techniques used in real risk teams.The course then expands into cloud-native architectures, showing you how financial institutions use AWS and Azure to ingest, transform, store, and govern large volumes of risk and finance data. You'll learn practical patterns such as raw → staging → curated → analytics layers, reconciliation controls, data quality checks, lineage, and auditability.Throughout the course, concepts are explained step-by-step from first principles, with a strong focus on practical implementation rather than theory. Every section is aligned with regulatory expectations, making this course highly relevant for professionals working in banking, fintech, consulting, or enterprise analytics.By the end of the course, you will be able to design and explain production-ready data engineering solutions for financial risk, bridging the gap between data engineering and business-critical decision-making.
Who this course is for
Risk analysts transitioning into data engineering
Data engineers working in banking, finance, or fintech
Credit risk, IFRS 9, Basel, and regulatory professionals
SQL/Python users who want real financial use cases
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