Data Engineering Portfolio Projects
Building portfolio projects for Data Engineering can be challenging outside enterprise environments due to limited access to realistic data, missing business context, and cloud costs.
Below are four practical portfolio projects that aspiring data engineers can build to showcase real-world skills. Each project focuses on a commonly used data engineering pattern and can be implemented using open-source tools or managed cloud services
1. Daily Sales Batch ETL
A Finance team requires an audit-ready daily sales report delivered every morning by 8:00 AM, based on the previous day’s completed orders. This is a classic batch data engineering scenario where data must be processed on a fixed schedule with strong guarantees around correctness, reproducibility, and scalability.
Architecture Overview
- Extract daily order data from the raw storage layer
- Transform sales data into clean, analytics-ready models
- Load curated tables for reporting and audits
- Schedule the pipeline to meet a strict daily SLA
Technology Stack
| Environment | Storage | Processing | Scheduling |
|---|---|---|---|
| Local / Open-Source | MinIO | Spark Docker | Airflow |
| AWS | Amazon S3 | AWS Glue | Glue Triggers |
| GCP | GCS | Cloud Dataflow | Cloud Scheduler |
Key points to consider
- Idempotent runs: Safe daily runs and reruns without duplication
- Incremental loading: Process only new or updated records using bookmarks
- Failure recovery & backfills: Reprocess data for specific dates as needed
- Schema evolution: Adapt to new columns or data type changes
- Partitioned, columnar storage: Efficient querying, and maintenance
2. Real-Time Order Monitoring
A Customer Support team needs immediate visibility into stuck orders (e.g., payment complete but products not shipped) to intervene before customers churn. This is a classic real-time operational use case, where events must be processed as they occur with guarantees for correctness and timeliness.
Architecture Overview
- Capture Events: Track order updates in near real-time from transactional systems using Change Data Capture (CDC)
- Process Stream: Transform, deduplicate, and aggregate events as they arrive
- Persist & Query: Store curated streams or aggregates for dashboards and alerts
- Alert / Monitor: Trigger notifications for stuck orders or SLA violations
Technology Stack
| Environment | Event Capture / CDC | Stream Processing | Storage / Query | Alerting / Monitoring |
|---|---|---|---|---|
| Local / Open-Source | Debezium + Kafka | Spark Structured Streaming | MinIO + DuckDB | Python / Spark triggers, Prometheus + Grafana |
| AWS | DMS + Kinesis | AWS Glue Streaming | S3 + Athena | CloudWatch / SNS |
| GCP | Datastream + Pub/Sub (CDC) | Cloud Dataflow | GCS + BigQuery | Cloud Monitoring + Pub/Sub alerts |
Key points to consider
- Late-arriving/Out-of-order events: Watermarks for delayed events and back-filling
- Deduplication: Exactly-once processing, despite receiving duplicates from source
- Partial/missing events handling: Robust to incomplete or missing sequences
- Windowed aggregations: Real-time metrics over fixed or sliding time windows
3. Campaign Performance Data Analytics
A Marketing team runs campaigns across Google Ads, Meta, and Email. They need a single source of truth to consistently analyse total spend, conversions, and campaign performance across channels. This is a classic analytics engineering use case, where raw ingestion data is transformed into curated, analysis-ready models.
Architecture Overview
- Storage (Bronze): Capture unprocessed campaign and conversion data.
- Transformation (Silver): Clean, standardize, enrich, and apply business logic
- Data Warehouse (Gold): Aggregate metrics at campaign and channel level for reporting and product analytics.
- Orchestration & Consumption: Automate daily ETL runs and query from BI tools.
Technology Stack
| Environment | Storage | Processing | Data Warehouse / Product Layer |
|---|---|---|---|
| Local / Open-Source | DuckDB / MinIO | Spark Docker | DuckDB |
| AWS | S3 | AWS Glue | Redshift |
| GCP | GCS | Cloud Dataflow | BigQuery |
Key points to consider
- Schema evolution detection & data contracts: Prevent broken transformations due to upstream changes
- Dimension modelling: Using Star/Snowflake schemas, SCD Type 2, and surrogate keys for historical tracking
- Data integrity & quality checks: Handle missing, malformed, or inconsistent records, backfill specific dates without duplication
- Consistent metric definitions: Ensure KPIs (spend, conversions, ROI) are reliable
4. Real-Time IoT Sensor Analytics
A factory floor needs to monitor high-frequency IoT sensor data to detect overheating machines or abnormal energy usage before equipment fails. This is a stateful streaming use case, where it is critical to compute averages, trends, and anomalies in real time.
Architecture Overview
- Ingestion: Capture sensor readings continuously from IoT devices or message streams
- Stream Processing: Maintain state, compute rolling averages, windowed aggregations, and detect anomalies
- Storage: Persist aggregated or processed sensor data for operational and historical use
- Monitoring & Alerting: Visualize metrics and trigger alerts on abnormal conditions
Technology Stack
| Environment | Ingestion | Stream Processing | Storage / Query | Monitoring & Alerting |
|---|---|---|---|---|
| Local / Open-Source | Kafka | Apache Flink | InfluxDB | Grafana / Python triggers |
| AWS | Kinesis | Managed Service Apache Flink | Timestream | CloudWatch / SNS |
| GCP | Pub/Sub | Dataproc for Apache Flink | Cloud Bigtable | Cloud Monitoring / Pub/Sub alerts |
Key points to consider
- Event-time processing & watermarks: Handles late or out-of-order readings
- Stateful rolling computations: Maintains averages, trends, and windowed metrics efficiently
- Dynamic anomaly detection: Configurable thresholds or statistical models per sensor
- High-throughput resilience: Processes large volumes of events without data loss
- Reliable alerting: Minimizes false positives while triggering timely notifications
Useful tips
- Use publicly available data sources like Kaggle, open APIs, or public cloud datasets
- Uber Data Analytics Dashboard Dataset
- Retail Data Analytics Dataset
- Free Real Time APIs
- Generate synthetic data (using Faker or GenAI) to simulate scale and edge cases
- Faker API
- Provision infrastructure using IaC (Terraform, CloudFormation, or YAML configs)
- Check-In code into GitHub repository and document architecture, data flow, assumptions and trade-offs in a README