The best data engineering portfolio projects demonstrate production thinking — error handling, idempotency, documentation, and scale awareness. This guide covers 15 projects across three tiers: 5 beginner (API ingestion, data quality, Airflow DAGs), 5 intermediate (streaming, medallion architecture, dbt, CI/CD), and 5 advanced (production streaming, data mesh, feature stores, governance). Each project includes the tech stack, what it proves to hiring managers, and how to present it on GitHub.
- Build 15 data engineering projects across beginner, intermediate, and advanced tiers
- Understand what makes a project portfolio-worthy vs a tutorial exercise
- Choose the right tech stack for each project type
- Present projects on GitHub and your resume to maximize hiring impact
- Apply core data systems principles (reliability, scalability, maintainability) to every project
Quick Answers
What projects should a data engineer have in their portfolio?
At minimum: one batch ETL pipeline (API to warehouse), one streaming pipeline (Kafka or similar), and one data transformation project (dbt or Spark). Each should handle real data, include error handling, and have a documented GitHub README with an architecture diagram.
How many data engineering projects do I need for a job?
Three to five well-documented projects are enough. Quality matters far more than quantity. One production-grade pipeline with tests, monitoring, and documentation impresses more than ten tutorial follow-alongs with no error handling.
What makes a data engineering project stand out to hiring managers?
Four things: it handles real data (not toy datasets), it includes error handling and retry logic, it has documentation (README with architecture diagram), and it demonstrates awareness of scale — even if the actual data volume is small.
Should I use AWS, Azure, or GCP for my portfolio projects?
Match the cloud to your target job market. Check 20 job postings — if most mention AWS services, build on AWS. All three major clouds have free tiers sufficient for portfolio projects. If unsure, AWS has the broadest job market reach.
Most "data engineering project ideas" lists give the same advice: build an ETL pipeline, use Airflow, load data into a warehouse. Those projects are fine for learning — but they don't get you hired. What separates a portfolio project from a tutorial exercise is production thinking: how does the pipeline handle failures? What happens when the source schema changes? Where's the documentation?
Martin Kleppmann's Designing Data-Intensive Applications frames this well: every data system must balance three concerns — reliability (continuing to work correctly when things go wrong), scalability (handling growth in data volume or traffic), and maintainability (making it easy for others to work with the system over time). Portfolio projects that demonstrate these three qualities stand out to hiring managers because they mirror real production systems.
Careery is an AI-driven career acceleration service that helps professionals land high-paying jobs and get promoted faster through job search automation, personal branding, and real-world hiring psychology.
Learn how Careery can help youWhat Makes a Data Engineer Project Portfolio-Worthy?
- Portfolio-Worthy Data Engineering Project
A project that demonstrates production-level thinking — including error handling, idempotency, documentation, and awareness of data system trade-offs — not just a working pipeline that runs once on clean data.
Hiring managers reviewing GitHub portfolios look for four signals:
1. It Handles Real Data
Real data is messy. APIs return unexpected formats, CSV files have encoding issues, timestamps come in different zones. Projects using curated tutorial datasets (Iris, Titanic, NYC Taxi) signal that the builder hasn't faced the challenges that dominate actual data engineering work.
What to do instead: Pull from public APIs (weather services, government open data, financial markets), scrape real websites, or combine multiple data sources with different schemas.
2. It Includes Error Handling and Retry Logic
A pipeline that works perfectly on the happy path proves nothing. Production pipelines fail — APIs time out, databases hit connection limits, source schemas change without notice.
What to show: Try/except blocks with meaningful logging. Retry logic with exponential backoff. Schema validation at ingestion. Graceful degradation when a source is unavailable.
3. It Has Documentation
A README with "run python main.py" is not documentation. Hiring managers want to see:
- Architecture diagram — a visual showing the data flow from source to destination
- Tech stack — what tools and why those were chosen
- How to run it — setup instructions that actually work
- Design decisions — why batch instead of streaming? Why Parquet instead of CSV?
4. It Demonstrates Scale Awareness
The project doesn't need to process terabytes. But it should show awareness of what would change at scale: partitioning strategies, incremental loading instead of full refreshes, efficient file formats (Parquet over CSV).
- Uses real data from APIs, web scraping, or public datasets — not tutorial datasets
- Handles errors: try/except, retry logic, schema validation, logging
- Includes a README with architecture diagram, tech stack, and setup instructions
- Uses efficient data formats (Parquet, Delta, Avro) — not just CSV
- Implements incremental loading — not full table refreshes every run
- Has tests (unit tests for transformations, integration tests for pipeline)
- Hosted on GitHub with clear commit history (not one giant commit)
The difference between a tutorial project and a portfolio project is production thinking — error handling, documentation, and design decisions that show awareness of real-world data systems challenges.
Beginner Projects (1–5)
These projects build foundational skills: data extraction, loading, basic transformation, scheduling, and data quality. Each can be completed in 1–2 weeks.
REST API → PostgreSQL ETL Pipeline
What to build: A Python script that pulls data from a public REST API (weather data, stock prices, or government datasets), transforms it, and loads it into a PostgreSQL database on a schedule.
Tech stack: Python, requests, psycopg2 or SQLAlchemy, PostgreSQL, cron or schedule library
What it proves: You can extract data from external sources, handle JSON parsing, write to a relational database, and schedule recurring jobs.
Production touches to add:
- Retry logic with exponential backoff for API failures
- Logging to a file (not just
print()statements) - Idempotent inserts — running the pipeline twice doesn't create duplicates
- Environment variables for API keys and database credentials (never hardcode secrets)
Data Quality Checker with Automated Reports
What to build: A pipeline that ingests data from a CSV or API source, runs data quality checks (null counts, type validation, range checks, uniqueness constraints), and generates a quality report.
Tech stack: Python, pandas, Great Expectations (or custom validation), Jinja2 for HTML reports
What it proves: You understand that data quality is a first-class concern in data engineering — not an afterthought. Great Expectations is widely used in production environments.
Production touches to add:
- Configurable expectations (not hardcoded thresholds)
- Historical quality tracking — store results over time to detect drift
- Alerting when quality drops below thresholds (even a simple email or Slack webhook)
Web Scraping → SQLite Data Warehouse
What to build: A scraper that collects structured data from a public website (job listings, product prices, event schedules), normalizes it, and loads it into a SQLite database with a star schema.
Tech stack: Python, BeautifulSoup or Scrapy, SQLite, scheduling (cron or Airflow)
What it proves: You can handle unstructured or semi-structured source data, design a dimensional model, and build an automated collection pipeline.
Production touches to add:
- Respect
robots.txtand rate limiting - Handle page structure changes gracefully (don't crash on missing elements)
- Track schema changes over time with a
_loaded_attimestamp
Simple Airflow DAG with Multiple Sources
What to build: An Apache Airflow DAG that orchestrates data extraction from 3+ different sources (API, CSV file, database), transforms the data, and loads it into a single target (PostgreSQL or S3).
Tech stack: Apache Airflow, Python, PostgreSQL or S3, Docker (for local Airflow deployment)
What it proves: You can work with a production orchestration tool that most data teams use. Airflow appears in the majority of data engineering job descriptions.
Production touches to add:
- Proper task dependencies (not linear — show parallel extraction where possible)
- Retry policies and failure alerts on individual tasks
- XCom for passing metadata between tasks (not data — keep payloads small)
- A clear DAG structure with meaningful task IDs
Database Change Tracking Pipeline (CDC Intro)
What to build: A pipeline that captures changes from a source database (inserts, updates, deletes) and replicates them to a target. This introduces Change Data Capture (CDC) concepts — a foundational pattern in data engineering.
Tech stack: PostgreSQL (with logical replication or trigger-based CDC), Python, target database or file storage
What it proves: You understand that real pipelines don't re-extract entire tables — they capture changes. CDC is a core concept discussed extensively in stream processing literature as a way to keep derived data systems in sync with source databases.
Production touches to add:
- Track the last processed change (offset or timestamp) for resumability
- Handle deletes properly (soft deletes vs hard deletes)
- Log all captured changes for audit purposes
Not sure where these projects fit in your overall journey? See our guide: How to Become a Data Engineer: Complete Career Guide. It covers the full path from first project to first job.
A Data Engineer at Optum (UnitedHealth Group) shares the exact projects and skills that took him from junior to leading critical data initiatives — including a JSON-to-Airflow DAG framework that saved $120K annually and Kafka-based real-time streams for healthcare data from 20+ US states: Data Engineer Roadmap from an Optum Engineer.
Beginner projects should demonstrate that you can extract, transform, and load data reliably. The differentiator is error handling, scheduling, and documentation — not complexity.
Intermediate Projects (6–10)
These projects introduce distributed systems, streaming, cloud infrastructure, and testing practices. Each takes 2–4 weeks.
Multi-Source Data Integration with Medallion Architecture
What to build: A data pipeline that ingests data from 3+ heterogeneous sources into a medallion architecture (bronze → silver → gold layers). Bronze stores raw data, silver applies cleaning and conforming, gold provides business-ready aggregations.
Tech stack: Apache Spark or PySpark, Delta Lake, S3 or Azure Blob Storage, Python
What it proves: You can design a layered data architecture that separates concerns — raw ingestion from transformation from business logic. The medallion pattern is the standard architecture at Databricks-centric organizations and beyond.
Production touches to add:
- Schema enforcement at each layer transition
- Data lineage tracking (which source record produced which gold table row)
- Partition strategy based on query patterns (date partitioning for time-series, hash for lookups)
- Idempotent writes using Delta Lake's MERGE capability
Want to see how a production medallion pipeline looks at scale? A Senior Data Engineer at GAP breaks down his Bronze → Silver → Gold implementation processing 5TB+ of retail data daily on Azure Databricks: Medallion Architecture: Complete Guide from a GAP Data Engineer.
Real-Time Streaming Pipeline with Kafka
What to build: A pipeline that produces events to a Kafka topic (simulated or from a real source like Twitter/X API or WebSocket feeds), consumes them with a Python consumer, transforms the data, and writes to a sink (PostgreSQL, Elasticsearch, or S3).
Tech stack: Apache Kafka, Python (confluent-kafka), Docker Compose (for local Kafka cluster), PostgreSQL or Elasticsearch
What it proves: You can work with event-driven architectures. Stream processing is increasingly central to data engineering — understanding message ordering, consumer groups, and offset management separates data engineers from data analysts.
Production touches to add:
- At-least-once delivery with idempotent consumers
- Dead letter queue for malformed messages
- Consumer lag monitoring
- Graceful shutdown handling
dbt Transformation Layer on Snowflake or BigQuery
What to build: A dbt project that transforms raw data (already loaded into a warehouse) into analytics-ready models. Include staging models, intermediate transformations, and mart-level aggregations.
Tech stack: dbt Core or dbt Cloud, Snowflake or BigQuery (free tier), SQL, YAML for schema definitions
What it proves: You can build a production-grade transformation layer with testing, documentation, and modularity. dbt is the standard tool for SQL-based transformation in the modern data stack.
Production touches to add:
- Data tests on every model (not_null, unique, accepted_values, relationships)
- Source freshness checks
- Documentation with
descriptionfields in YAML - Incremental models (not just full refreshes) for large tables
Data Lake on S3 with Glue Catalog and Athena
What to build: An AWS-based data lake that ingests data into S3 in Parquet format, registers tables in the AWS Glue Data Catalog, and enables serverless querying through Athena.
Tech stack: AWS S3, AWS Glue (ETL jobs and Crawler), AWS Athena, Parquet or Apache Iceberg, Python (boto3)
What it proves: You can architect a cloud-native data lake with metadata management and serverless query capabilities. This project directly maps to AWS Certified Data Engineer exam content.
Production touches to add:
- Partition pruning strategy (year/month/day for time-series data)
- S3 lifecycle policies (move old data to Glacier)
- Glue job bookmarks for incremental processing
- Cost tracking with S3 Storage Lens
Projects 4, 5, and 9 directly align with the AWS Certified Data Engineer exam domains. See our guide: AWS Data Engineer Certification (DEA-C01): Complete Guide.
CI/CD for Data Pipelines
What to build: A CI/CD system for one of your earlier projects — automated testing, linting, and deployment triggered by Git pushes.
Tech stack: GitHub Actions, pytest, dbt test (if applicable), Docker, pre-commit hooks
What it proves: You treat data pipelines like software — with version control, automated testing, and deployment automation. This is a strong signal of engineering maturity.
Production touches to add:
- Unit tests for transformation logic
- Integration tests that run against a test database
- SQL linting (sqlfluff)
- Automated deployment to staging → production with approval gates
Intermediate projects should demonstrate distributed systems awareness, cloud infrastructure skills, and software engineering practices (testing, CI/CD). These are the skills that separate data engineers from data analysts.
Advanced Projects (11–15)
These projects tackle production-grade systems: exactly-once semantics, data mesh architecture, feature engineering, cost optimization, and governance. Each takes 3–6 weeks.
Production-Grade Streaming Analytics Platform
What to build: An end-to-end streaming platform that ingests high-volume events, processes them with exactly-once or effectively-once semantics, and serves real-time aggregations to a dashboard.
Tech stack: Apache Kafka, Apache Flink or Spark Structured Streaming, PostgreSQL or ClickHouse (for real-time aggregations), Grafana (for dashboards), Docker Compose
What it proves: You can build a system that handles the hardest problem in stream processing — maintaining correctness under failures. Exactly-once delivery requires understanding checkpointing, idempotent writes, and transaction boundaries — concepts that are central to building reliable distributed data systems.
Production touches to add:
- Checkpointing and state recovery after consumer failures
- Watermarking for handling late-arriving events
- Backpressure handling when the sink is slower than the source
- Monitoring dashboard showing throughput, latency, and consumer lag
Data Mesh Domain Implementation
What to build: A prototype of a data mesh domain — a self-contained data product that owns its own ingestion, transformation, and serving layers, with published data contracts and discovery metadata.
Tech stack: dbt (transformation), Great Expectations (contracts), S3 or GCS (storage), a metadata catalog (DataHub or simple JSON schema registry), API layer for data serving
What it proves: You understand the organizational and architectural shift from centralized data teams to domain-owned data products. This is a concept explored in discussions about the future of data systems — moving from monolithic architectures to composable, independently operated data services.
Production touches to add:
- Published schema contract (JSON Schema or Protobuf) with versioning
- SLA definitions (freshness, completeness, accuracy)
- Self-serve discovery (other teams can find and use your data product)
- Change notification when the schema evolves
ML Feature Store Pipeline
What to build: A feature engineering and serving pipeline that computes features from raw data, stores them in a feature store, and serves them for both training (batch) and inference (real-time).
Tech stack: Feast (open-source feature store) or custom implementation, Spark or pandas for feature computation, Redis or DynamoDB for online serving, S3 or BigQuery for offline store
What it proves: You can bridge data engineering and machine learning infrastructure — a skill set in high demand as ML engineering grows. Feature stores require understanding both batch and real-time data pipelines.
Production touches to add:
- Feature versioning (same feature, different computation logic over time)
- Point-in-time correct joins for training data (avoid data leakage)
- Feature freshness monitoring
- Documentation of feature definitions and business logic
Cost-Optimized Cloud Data Platform
What to build: A data platform on AWS, Azure, or GCP designed around cost efficiency — implementing lifecycle policies, query optimization, partitioning strategies, and resource scheduling to minimize cloud spend.
Tech stack: Cloud provider of choice, Terraform or Pulumi (infrastructure as code), S3/GCS lifecycle policies, reserved/spot instances, cost monitoring (AWS Cost Explorer or similar)
What it proves: You understand that cloud costs are a primary concern for data teams. The ability to design cost-efficient systems — through proper partitioning, compression, lifecycle management, and right-sizing compute — is highly valued at senior levels.
Production touches to add:
- Infrastructure as code (everything reproducible via Terraform)
- Cost alerting with budget thresholds
- Comparison report: full scan vs partitioned query costs
- Auto-scaling or scheduled compute (don't run Spark clusters 24/7)
Data Governance Framework
What to build: A governance layer for one of your earlier projects — implementing access control, data lineage tracking, classification (PII detection), and audit logging.
Tech stack: Unity Catalog (Databricks) or Apache Atlas, Python, SQL, OpenLineage for lineage, custom PII scanner
What it proves: You understand that data governance is an engineering problem, not just a compliance checkbox. As regulations (GDPR, CCPA) and data security requirements grow, governance skills are increasingly required for senior data engineering roles.
Production touches to add:
- Role-based access control (who can read which tables)
- Automated PII detection and masking
- Column-level lineage (which source columns feed which target columns)
- Audit log of all data access
Advanced projects 11–15 align directly with certification exam topics — Kafka/streaming for AWS DEA-C01, Unity Catalog for Databricks DE, governance for all three. See our full comparison: Best Data Engineering Certifications.
Advanced projects should demonstrate architectural thinking — trade-off analysis, cost awareness, and governance. These are the skills that lead to senior and staff-level roles.
Project Complexity Matrix
How to Present Projects on GitHub and Your Resume
GitHub README Template
Every project repository should include a README with these sections:
# Project Name ## Overview One paragraph describing what this pipeline does, what data it processes, and why. ## Architecture [Include a diagram — even a simple Mermaid or draw.io diagram] Source → Ingestion → Transformation → Storage → Serving ## Tech Stack - **Ingestion:** [tool/library] - **Transformation:** [tool/library] - **Storage:** [database/data lake] - **Orchestration:** [Airflow/cron/etc.] - **Testing:** [pytest/Great Expectations/dbt test] ## How to Run 1. Clone the repo 2. Copy .env.example to .env and fill in credentials 3. docker-compose up -d 4. python main.py ## Design Decisions - Why [tool X] over [tool Y]? - Why this partitioning strategy? - How does the pipeline handle [specific failure mode]? ## Data Quality - What checks are in place? - How are failures handled? ## What I'd Do Differently at Scale - [Scaling considerations] - [Production improvements]
Resume Bullet Formula
For each project on your resume, use this formula: action verb + what you built + technical specifics + scale/impact.
For the complete guide on writing data engineer resume bullets, ATS optimization, and listing certifications, see our Data Engineer Resume Guide.
Strong portfolio projects — especially at the intermediate and advanced tiers — correlate with higher compensation. See where you stand: Data Engineer Salary Guide 2026.
Presentation matters as much as the project itself. A clear README with an architecture diagram and design decisions turns a code repository into a career asset.
Common Portfolio Mistakes
- Following a YouTube tutorial line-by-line and pushing it as 'your project' — hiring managers can tell (and they Google the tutorial title)
- Using only toy datasets (Iris, Titanic) that don't demonstrate real-world data challenges
- No error handling — the pipeline works once on clean data and breaks on everything else
- One giant commit with the message 'initial commit' — show your development process through meaningful commit history
- No README or a README that says 'run python main.py' — this signals you've never worked in a team
- Skipping tests entirely — data engineers are expected to treat pipelines as software
Key Takeaways
- 1Portfolio projects need production thinking: error handling, idempotency, documentation, and scale awareness
- 2Start with 3–5 projects across beginner and intermediate tiers — quality over quantity
- 3Every project should have a clear README with architecture diagram, tech stack, and design decisions
- 4Match your project tech stack to your target job market (AWS, Azure, GCP, Databricks)
- 5Beginner projects prove you can ETL reliably; intermediate projects prove distributed systems and cloud skills; advanced projects prove architectural thinking
- 6Present projects with strong resume bullets: action verb + what you built + tech specifics + scale/impact
- 7Certifications complement projects — they validate the knowledge, projects demonstrate the application
Frequently Asked Questions
Can I use personal data engineering projects instead of work experience?
Yes, especially for career changers and junior engineers. Hiring managers evaluate portfolio projects as evidence of capability. Three well-documented projects with production-level code quality can substitute for entry-level work experience on a resume.
What's the best free dataset for data engineering projects?
Government open data portals (data.gov, NYC Open Data) provide real, messy data that's free and legal to use. Public APIs (OpenWeather, CoinGecko, GitHub API) are excellent for building ingestion pipelines. Avoid curated Kaggle datasets — they're too clean to demonstrate real data engineering challenges.
Should I build data engineering projects on AWS, Azure, or GCP?
Match the cloud to your target job market. Check 20 job postings and count which cloud appears most. All three have free tiers sufficient for portfolio projects. If unsure, AWS has the broadest market reach. Building on multiple clouds is unnecessary — one cloud plus transferable skills (SQL, Python, Spark) covers most jobs.
How important is Docker for data engineering projects?
Very important. Docker ensures your project runs on any machine, which is critical for both hiring managers evaluating your code and real production systems. At minimum, include a Dockerfile for your application and docker-compose for local infrastructure (databases, Kafka, Airflow).
Do I need Spark for a data engineering portfolio?
Not for beginner or most intermediate roles. Python + SQL covers 80% of data engineering work. However, Spark (PySpark) appears in most mid-level and senior job descriptions. If you're targeting roles above entry-level, at least one Spark project (Project 6 or 11) demonstrates distributed data processing skills.
Should I deploy my portfolio projects to the cloud or keep them local?
Having at least one project deployed to a cloud provider (even on free tier) is a strong signal. It shows you can work with cloud services, IAM, networking, and deployment — skills that many candidates only claim but can't demonstrate. Use infrastructure as code (Terraform) for bonus points.
Researching Job Market & Building AI Tools for careerists since December 2020
Sources & References
- Designing Data-Intensive Applications — Martin Kleppmann (2017)
- Apache Kafka Documentation — Apache Software Foundation (2026)
- Apache Airflow Documentation — Apache Software Foundation (2026)
- dbt Documentation — dbt Labs (2026)
- Great Expectations Documentation — Great Expectations (2026)
- Delta Lake Documentation — Delta Lake Project (Linux Foundation) (2026)