What is Data Engineering & Platforms?
Data Engineering & Platforms is about building the reliable pipelines, storage, and processing layers that turn raw data into trustworthy, usable datasets. Data Engineers design models and tables, move and transform data at scale, ensure quality, and make it easy and safe for analysts, scientists, and products to use data.
Typical problems solved
- Ingesting data from APIs, databases, files, and streaming sources
- Designing data models and warehouses so data is easy to query
- Transforming raw data into clean, standardized datasets
- Scheduling and orchestrating pipelines that run daily or in real time
- Ensuring data quality, lineage, observability, and cost control
- Providing secure, governed access to datasets across teams
Who this is for
- Builders who enjoy creating systems that run reliably
- People who like SQL, scripting, and automating repetitive tasks
- Detail-oriented problem-solvers who care about correctness and speed
- Those who want impact across many teams by enabling analytics and ML
Prerequisites
- Comfort with basic SQL (SELECT, WHERE, GROUP BY, JOIN)
- Basic Python or similar scripting language
- Familiarity with CSV/JSON and working in a terminal
- Optional but helpful: Git basics and any cloud exposure
Learning path (at a glance)
- Strengthen SQL and data modeling fundamentals
- Automate transformations with Python and a workflow tool
- Learn warehouse/lake concepts and partitioning
- Add testing, data quality checks, and documentation
- Deploy to the cloud or a local equivalent; add monitoring
Mini task: Normalize a messy dataset
Take a CSV with duplicate users and inconsistent timestamps. Write SQL to de-duplicate by a primary key, standardize time zones to UTC, and produce a clean dimension table.
Careers inside this direction
- Data Engineer – Builds and maintains data pipelines, storage, and processing systems that make reliable, timely data available for analytics and products. Best for: people who enjoy building systems, are comfortable with SQL/Python, and value reliability.
Where you can work
- Industries: tech, fintech, e-commerce, healthcare, gaming, media, logistics, travel, government, energy
- Company types: startups, scale-ups, enterprises, consultancies, data platform vendors, nonprofits
- Team setups: central data/platform teams, embedded product teams, data platform groups in large orgs
Salary ranges by stage
- Junior Data Engineer: ~$60k–95k
- Mid-level Data Engineer: ~$95k–140k
- Senior/Staff Data Engineer: ~$140k–200k+
Varies by country/company; treat as rough ranges.
Growth map
- Junior → Mid: solid SQL, reliable ETL/ELT jobs, basic data modeling, version control, simple tests
- Mid → Senior: end-to-end ownership, cost/perf tuning, workflow orchestration, governance, observability
- Senior → Staff/Lead: platform design, data contracts, multi-team architecture, SLAs/SLOs, mentoring
Signals you are ready for the next level
- You prevent issues with tests and design, not just fix them after alerts
- You make trade-offs explicit: cost vs freshness vs complexity
- Others rely on your patterns and documentation to move faster
Tools & stack overview
- Languages: SQL, Python (sometimes Scala/Java for Spark-heavy stacks)
- Storage: PostgreSQL/MySQL, object storage (S3/Blob/GCS), data warehouses (Snowflake, BigQuery, Redshift)
- Processing: dbt for transformations, Spark for big data, streaming with Kafka/Kinesis/PubSub
- Orchestration: Airflow, Prefect, Dagster
- Containers/Infra: Docker, Kubernetes (optional at start)
- Quality/Observability: tests in SQL/Python, Great Expectations/Soda-like checks, basic lineage
- Collaboration: Git, pull requests, code reviews, documentation
Choosing a beginner-friendly stack
- Warehouse: start with a free-tier or local Postgres
- Transformations: dbt Core + SQL models
- Orchestration: Prefect or Airflow locally
- Storage: local files for practice; understand object storage concepts
Beginner roadmap (4–8 weeks)
Week 1: SQL core
- Practice SELECT, filtering, aggregation, window functions
- Write joins (inner/left) and handle NULLs explicitly
- Mini task: build a customer metrics query (orders, revenue, first_seen)
Week 2: Python for data
- Read/write CSV/JSON, work with datetime and time zones
- Call a REST API and save responses incrementally
- Mini task: fetch daily data and append only new records
Week 3: Data modeling
- Star schema basics: facts, dimensions, slowly changing dimensions (simple)
- Design staging, core, marts layers
- Mini task: sketch a simple warehouse diagram and build 2–3 tables
Week 4: Orchestration & ELT
- Schedule a daily job that extracts data, loads to warehouse, runs transforms
- Add retries, logging, and parameterized runs
- Mini task: one-click run that rebuilds a daily sales mart
Week 5: Quality, docs, and monitoring
- Add tests (not null, unique, foreign keys) and freshness checks
- Document tables and columns with clear owners and purposes
- Mini task: add anomaly alert when daily volume drops by 30%+
Week 6: Cloud concepts or local equivalents
- Understand object storage, compute, networking basics, and IAM concepts
- Practice deploying a simple pipeline to run on a schedule
- Mini task: cost-aware design (partitioning, compression, pruning)
Stretch Weeks 7–8: Streaming and optimization
- Build a small streaming ingest and transform job
- Tune a heavy query with partitions, clustering, and indexes
Common mistakes
- Skipping tests and documentation, causing breakages and rework
- Over-engineering early; keep designs simple and evolve with needs
- Ignoring costs and data freshness trade-offs
- Pipelines that only work on your machine; automate and parametrize
- Unclear ownership; define data contracts and consumers
Mini project ideas
- CSV to warehouse: Load daily CSVs into a staging table, clean them, and publish a mart
- API ingestion: Incrementally fetch a public API and build a simple fact and two dimensions
- Log parser: Parse app logs into events and sessions, with a daily sessionization job
- Quality checks: Add not-null/unique tests and a freshness check with a simple alert
- Cost-aware partitioning: Partition a large fact table by date and compare scan sizes
Practical projects
Project 1: End-to-end sales analytics platform
- Extract: API + CSV ingest to staging
- Transform: build star schema (orders, order_items, customers, products)
- Orchestrate: daily schedule with retries and logging
- Quality: tests for keys, freshness, and basic anomaly checks
- Deliver: a daily revenue dashboard dataset (by day, product, channel)
Project 2: Streaming events to feature store
- Ingest clickstream events
- Aggregate rolling 7-day counts per user in near-real-time
- Publish a features table and document data contracts
Project 3: Data reliability toolkit
- Add schema and null checks to 5 critical tables
- Set up freshness SLAs and a weekly lineage review
- Create runbooks for common incidents and on-call handoff notes
Next steps
- Take the quick fit test on this page to gauge your match
- Choose the Data Engineer path and commit to the 6-week roadmap
- Start a practical project and iterate with tests, docs, and monitoring
- When ready, explore the professions section below to focus your journey
Note: Anyone can take the fit test for free; if you log in, your progress will be saved.