Senior-to-Lead Data Engineering

Overview

This knowledge base is designed for experienced Data Engineers (10+ years) transitioning to Senior Staff Engineer, Engineering Manager, Lead Architect, or Principal Data Engineer roles. Content reflects 2024/2025 industry standards with forward-looking insights into 2026 trends, focusing on Terabyte-to-Petabyte scale systems with high throughput and low latency requirements.

Target Audience

• Senior Data Engineers → Staff/Principal Engineers
• Tech Leads → Engineering Managers
• Data Architects → Principal Data Architects
• Professionals designing TB/PB-scale data systems

Scale Focus

Metric	Target Range
Data Volume	Terabytes to Petabytes
Throughput	Millions of events/sec
Latency	Milliseconds to seconds
Concurrency	Thousands of concurrent queries
Availability	99.9% to 99.999% SLA

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Module Navigation

Module	Focus Area	Key Topics
00 - Foundation	Role Transition & Prerequisites	Glossary, role transitions, prerequisites
01 - Modern Data Architecture	Lakehouse & Table Formats	Delta/Iceberg/Hudi, Parquet, compute engines
02 - Computing & Processing	Batch & Streaming	PySpark, Kafka/Pulsar, Flink, Rust
03 - Cloud Infrastructure	Cloud & Orchestration	AWS/GCP/Azure, IaC, Airflow/Dagster
04 - Data Modeling & Warehousing	Modeling & Quality	Kimball, dbt, data contracts, governance
05 - AI/ML & Vector Databases	LLM Ops & Vectors	RAG, embeddings, feature stores
06 - CI/CD for Data	Data DevOps	CI/CD pipelines, data diffing
07 - Performance & Cost	Optimization	Compaction, compression, FinOps
08 - Case Studies	Real-World Scenarios	FinTech, Healthcare, AdTech, IoT
09 - Career & Interview Strategy	Career Advancement	System design, leadership
10 - References	Further Learning	Certifications, community

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Content Structure

Each module follows a consistent template:

1. Core Concepts - Deep theoretical understanding
2. Architecture & Design - Best practices and anti-patterns
3. Tech Stack Integration - How tools fit together
4. Performance & Cost Implications - Impact on speed and budget
5. Senior Level “Gotchas” - Production pitfalls at scale
6. Architecture Diagrams - Mermaid JS visualizations

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Quick Start Paths

For Staff/Principal Engineers

1. Start with 01 - Modern Data Architecture
2. Deep dive into 07 - Performance & Cost
3. Review 08 - Case Studies
4. Study 09 - Interview Strategy

For Engineering Managers

1. Begin with 00 - Foundation (role transitions)
2. Focus on 04 - Data Modeling & Governance
3. Understand 03 - Cloud Infrastructure for planning
4. Review leadership scenarios in Module 9

For Architects

1. Master 01 - Modern Data Architecture
2. Study all 02 - Computing Patterns
3. Deep dive into 07 - Performance & Cost
4. Review all 08 - Case Studies for patterns

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Learning Principles

Theory First, Code Second

• Focus on architecture, trade-offs, and decision-making
• Code examples only for complex concepts
• Emphasis on “why” over “how”

Scale Mentality

• Every design decision considers TB/PB scale
• Performance implications are non-negotiable
• Cost is a first-class architectural concern

Production Reality

• Anti-patterns from real production failures
• Failure modes and recovery strategies
• SLA/SLI/SLO implementation patterns

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Content Statistics

Metric	Count
Markdown Files	~118 files
Word Count	100,000+ words
Mermaid Diagrams	76+ diagrams
Case Studies	7 detailed scenarios
Modules	10 comprehensive modules

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Last Updated: 2025 | Target: 2026 Standards