A free, honest reference for anyone building a data career: what the roles really are, a phase-by-phase roadmap, the 2026 modern data stack mapped out, the concepts and tools to master in each of nine tracks, and a curated reading list. Newbie or guru — start where you are.
Completely new? Don't buy a course yet. Start at Foundations — three months of SQL and Python is worth more than any tool. Then walk the roadmap phase by phase and build the milestone project at the end of each.
Already working with data? Skim the stack map to see what's changed in 2026 (Iceberg, Dagster, dlt, DuckDB), then use the nine tracks as a checklist to find and close your gaps.
One rule holds for everyone: learn by building. Every track here maps to hands-on exercises on EgyBI — real, cited Egyptian data instead of toy tables. The books are pointers; the reps are the point.
Three roles sit along one assembly line: raw data flows from the engineer, through the analytics engineer's models, to the analyst's answers. Know which one you're aiming for — the roadmap below serves all three, and they share the same foundations.
Moves and shapes data at scale: ingestion pipelines, storage, warehouses/lakehouses, streaming, and the reliability that keeps it all running in production.
~US$130k median · varies by marketThe bridge between engineering and analysis: turns raw warehouse tables into clean, tested, documented models the whole business can trust.
~US$150k+ median · fast-growing roleUses the models to answer business questions — dashboards, reports, and the story behind the numbers that drives decisions.
~US$105k median · the classic entry pointAdjacent roles share the same base: Data Scientist (stats + ML on top of clean data), ML / AI Engineer (models in production), and Platform / DataOps Engineer (the infra the data team runs on). Pay figures are US medians (Research.com / industry surveys, 2026) — adjust for your country; MENA and remote ranges differ.
A realistic order to learn things in — not a list of everything at once. Most career-changers reach job-ready in 6–9 months part-time (10–15 hrs/week), or 3–4 months full-time. Finish each phase with its project before moving on.
The bedrock every data role shares. Don't rush it — depth here pays off forever.
Assemble a real, modern pipeline end to end — the core of the job.
Make it big, reliable, and trustworthy — what separates juniors from engineers.
Ongoing. Pick a lane and grow toward senior / staff.
Tools change every year; these don't. Get comfortable here and every framework afterwards is just syntax.
The one language you'll use every day in every role. Go past SELECT: joins, GROUP BY/HAVING, subqueries, CTEs, window functions, and reading a query plan. Track 1 below is a full SQL path.
The glue of data engineering. Data structures, functions, files, error handling, and calling APIs. Then pandas/Polars for wrangling. You don't need to be a software architect — you need to be fluent.
Pipelines run on servers, not laptops. Navigate a filesystem, pipe commands, ssh, edit files, read logs, manage environments. A weekend gets you dangerous; you'll use it forever.
All modern data work is code — models, pipelines, configs — reviewed in pull requests. Learn clone / branch / commit / push / PR / merge. It's how teams ship without breaking each other.
The mental model: sources → ingestion → raw storage → transformation → warehouse/lakehouse → BI. Know why each layer exists before you learn its tools. The stack map below is exactly this.
Basic data structures & algorithms, a little networking (HTTP, ports), and spreadsheet fluency. You can pick these up as you go — don't let them stall you starting.
Every data platform is some arrangement of these layers. You don't need every tool — you need to understand what each layer does and one solid tool per layer. Named tools are today's common picks, not the only ones.
Get data out of sources (databases, APIs, files, events) and land it — batch or streaming (CDC).
Batch/EL: Fivetran, Airbyte, dlt · Streaming/CDC: Kafka, Flink, Debezium
Where raw and modeled data live. Object storage + open table formats now blur "lake" and "warehouse" into the lakehouse.
Object store: S3, GCS, ADLS · Table formats: Apache Iceberg (the 2026 default), Delta Lake, Hudi, DuckLake
The engines that actually crunch the data — from a single laptop to a thousand-node cluster.
Local/embedded: DuckDB, Polars · Distributed: Spark, Trino/Presto
Managed, columnar, massively-parallel query platforms — the analytics workhorse.
Warehouses: Snowflake, BigQuery, Databricks, Redshift
Turn raw tables into clean, tested, modeled datasets — as version-controlled code.
Transform: dbt (table stakes), SQLMesh · plus a semantic layer for metrics
Schedule, sequence, retry and monitor the whole pipeline — the conductor.
Orchestrators: Airflow (the incumbent), Dagster (asset-centric), Prefect, Kestra
Where data becomes decisions — dashboards, reports, and self-service analytics.
BI: Power BI, Tableau, Looker, Metabase, Superset
Trust is a layer too: testing, observability, lineage, cataloging and access control run across everything above.
Quality: dbt tests, Great Expectations, Soda, data contracts · Catalog: DataHub, OpenMetadata, Unity Catalog
Each track lists what it is, why it matters, the concepts to master (your checklist), the tools, a book pointer, and the hands-on EgyBI exercises — all on real, cited Egyptian data. They map to the three roles from the top of the page, so you can follow one path or borrow across all three. SQL underpins every path — start there whatever your goal.
For the engineer who moves and processes data at scale — ingestion, orchestration, distributed compute, and the cloud platforms it all runs on. Maps to the Data Engineer role.
Pipelines aren't scripts you run by hand — they're scheduled, retried, and monitored. Orchestration is the conductor.
Tools: Apache Airflow, Dagster, Prefect, Kestra.
Read: Data Pipelines with Apache Airflow (Harenslak et al.).
When data outgrows one machine, you go distributed. Spark is the batch engine behind most large-scale pipelines.
Tools: Spark/PySpark, Databricks, Parquet, Polars (single-node).
Read: Learning Spark (Damji et al.) · Designing Data-Intensive Applications (Kleppmann).
dim_dynasty against a large fact table.The managed platforms where modern analytics lives. Understanding how they store and bill data is what makes pipelines fast and cheap.
Tools: Snowflake, BigQuery, Databricks; Apache Iceberg.
Read: Snowflake: The Definitive Guide (Avila).
The language that ties the whole stack together — ingestion, glue, automation, and the pipelines the orchestrator runs.
Tools: Python 3, pandas/Polars, requests, dlt, pytest, SQLAlchemy.
Read: Data Engineering with Python (Crickard) · Fundamentals of Data Engineering (Reis & Housley).
For the builder in the middle — dimensional modeling, dbt transformations, and the tests and observability that make the numbers trustworthy. Maps to the Analytics Engineer role.
How you shape data so a business can actually use it. The design skill behind every fast, trustworthy warehouse.
Tools: any warehouse, dbt, an ERD tool.
Read: The Data Warehouse Toolkit (Kimball) · Building the Data Warehouse (Inmon) · Star Schema (Adamson).
fact_monument_construction against pharaoh, dynasty & location dimensions.dim_dynasty across monument and reign facts.The modern transformation layer — SQL, but version-controlled, tested, documented and modular. The heart of the analytics-engineer role.
Tools: dbt (Core/Cloud), a warehouse, Git.
Read: Analytics Engineering with SQL and dbt (Machado & Russa).
not_null / unique / relationships tests.A pipeline that silently ships wrong numbers is worse than one that's down. Trust is engineered — this is how.
Tools: dbt tests, Great Expectations, Soda, Monte Carlo.
Read: Data Quality Fundamentals (Moses, Gavish & Vorwerck).
For the analyst and BI developer — SQL is the foundation every path starts with, and here it is most of the job, paired with the visualization skills that turn a model into something a business acts on. Maps to the Data / BI Analyst and BI Developer roles.
The single most-used skill in data. Every role queries; the good ones query well. Master this before anything else.
Tools: PostgreSQL, any warehouse SQL, SQLite (in-browser here).
Read: SQL Antipatterns (Karwin) · SQL Performance Explained (Winand).
Data only matters when someone acts on it. BI turns models into dashboards, measures and stories a business runs on.
Tools: Power BI, Tableau, Looker, Metabase.
Read: Storytelling with Data (Knaflic).
Tools come and go; these concepts are what interviews probe and production demands. Understand the why and any new tool is easy.
Process data in scheduled chunks, or continuously as it arrives. Most teams run both — batch for history, streaming for now. Know the latency/cost trade-off.
Transform before loading, or load raw and transform inside the warehouse. Cheap cloud compute made ELT the modern default — the L before the T is why dbt exists.
Structured & fast, cheap & flexible, or both at once. Open table formats (Iceberg) gave the lake warehouse-grade reliability — the 2026 direction.
Running a pipeline twice must not double the data. Idempotent loads (upserts, partition overwrites) are what make retries and backfills safe.
A promised schema & meaning between a producer and its consumers, so an upstream change can't silently break downstream models. The 2026 answer to "who broke the dashboard?"
How data is split across files/nodes decides speed and cost. Moving it around (the shuffle) is expensive — designing to avoid it is half of performance tuning.
A short, respected library that covers the whole field. Read the first two cover to cover; use the rest as track references. (Titles & authors only — EgyBI's exercises are written from scratch, never copied.)
Free resources worth more than most courses: the official docs for dbt, Airflow/Dagster, Snowflake/BigQuery and Spark; open datasets (World Bank, the Met Museum's CC0 collection, and the cited data right here on EgyBI); and the data communities on Reddit, Discord and LinkedIn where practitioners debate this stuff daily.
No. A degree helps but isn't required — a strong portfolio of real pipelines and solid SQL/Python get interviews. Most hiring is skills-first now.
For data engineering, not much beyond comfort with numbers — that's more the data-scientist's world. You need logic, systems thinking and code. Stats matter if you later move toward data science.
SQL. It's the fastest to become useful with and every role uses it daily. Add Python right after; the two together are your foundation.
Any one of AWS / GCP / Azure teaches the concepts; they transfer. Free tiers plus tools that run locally (DuckDB, Postgres in Docker) let you learn the whole stack for almost nothing.
Build projects that look like real work: pull from an API, store raw files, transform with dbt, load to a warehouse, feed a dashboard — orchestrated and on a schedule. One end-to-end project beats ten tutorials. EgyBI's tracks are built to be exactly that.
It's changing the job, not deleting it — AI writes boilerplate, but someone still designs, tests, secures and runs the systems, and demand for clean, reliable data only grows as AI does. Learn to use the tools; own the judgment.
The roadmap only works if you build. Every track here has hands-on reps waiting — real, cited Egyptian data, auto-graded, in your browser.