Great — here are 10 hands-on data-engineering project ideas you can build to learn practical skills. For each project I give the objective, suggested tech stack, concrete steps to implement, sample data sources, estimated difficulty/time, learning outcomes, and optional extensions.

1. Simple ETL pipeline: ingest CSV → clean → load to Postgres

• Objective: Implement a repeatable ETL that reads raw CSV, applies transformations, and writes to a relational warehouse.
• Tech: Python (pandas), PostgreSQL (or SQLite for local), Docker, cron (or Airflow for automation).
• Steps: pick CSV(s) (e.g., NYC 311 calls), write extractor that downloads file, cleaner that normalizes columns/types and handles missing values, loader that upserts into Postgres, schedule to run daily, add logging and basic tests.
• Data: NYC OpenData, Kaggle (many CSVs).
• Difficulty/time: Beginner → 1–3 days.
• Outcomes: SQL basics, schema design, idempotent loads, basic scheduling, containerizing.
• Extensions: add schema migrations, CI tests, and health checks.

2. Incremental loads & CDC to a data warehouse

• Objective: Build incremental ingestion (only new or changed rows) into a warehouse to avoid full reloads.
• Tech: Python, PostgreSQL source, Snowflake/BigQuery/Redshift target (or Postgres), SQLAlchemy, with Airflow or Prefect.
• Steps: simulate source table with timestamps or change_log, implement incremental extractor using watermark or log-based approach, upsert into target, implement backfill and reconciliation query.
• Data: synthetic data or change-log-style CSVs.
• Difficulty/time: Intermediate → 3–5 days.
• Outcomes: upserts/merge, watermarking, data reconciliation, scheduling.
• Extensions: implement Debezium for real CDC from MySQL/Postgres.

3. Batch processing with Apache Spark

• Objective: Process large datasets with Spark (ETL/aggregation) and write out partitioned parquet datasets.
• Tech: PySpark or Scala Spark, local cluster via Docker or Databricks Community Edition, S3/minio for object storage, Parquet/ORC.
• Steps: pick big-ish dataset (e.g., NYC taxi trips), write Spark job to clean, transform, compute aggregates, write partitioned parquet, benchmark performance and tune parallelism.
• Data: NYC Taxi, Common Crawl subsets, Kaggle.
• Difficulty/time: Intermediate → 3–7 days.
• Outcomes: distributed processing, partitions, schema evolution, parquet benefits.
• Extensions: add Delta Lake for ACID and time travel.

4. Real-time ingestion pipeline with Kafka + stream processing

• Objective: Build a streaming pipeline: ingest events, process in real-time, and store results for analytics.
• Tech: Kafka (or Redpanda), Kafka Connect, stream processor (ksqlDB, Flink, or Spark Structured Streaming), sink to ClickHouse/Elasticsearch or data lake.
• Steps: produce synthetic event stream (user clicks, IoT), set up Kafka, create stream processors to filter/enrich/aggregate, sink processed output, visualize with Kibana/Dashboards.
• Data: synthetic clickstream or public streaming datasets.
• Difficulty/time: Advanced → 1–2 weeks.
• Outcomes: event-driven architecture, topics/partitions, retention, stream transformations, exactly-once concepts.
• Extensions: schema registry (Avro/Protobuf), enforce schema, consumer lag monitoring.

5. Batch + orchestration with Airflow (DAGs, sensors, XCom)

• Objective: Build a set of scheduled, dependent ETL tasks with visibility and retries.
• Tech: Apache Airflow (local Docker), Python operators, Postgres, S3.
• Steps: design DAGs for ETL steps (extract → transform → load → test), implement retries/SLAs, use sensors to wait for files, implement XCom for passing metadata, configure monitoring/alerts.
• Data: any project above can be orchestrated.
• Difficulty/time: Intermediate → 3–5 days.
• Outcomes: job orchestration, idempotency, task dependencies, observability.
• Extensions: KubernetesExecutor, integrate with cloud-managed Airflow.

6. Data lakehouse: ingest raw → bronze/silver/gold layers with dbt

• Objective: Build a layered data pipeline and use dbt for transformations and testing.
• Tech: S3/minio, Spark or Athena/BigQuery, dbt (core or cloud), Delta Lake or parquet, Git for versioning.
• Steps: ingest raw files to bronze, implement transformation models in dbt for silver/gold, write tests and documentation, create lineage, expose SQL-accessible views.
• Data: NYC Taxi, GitHub event logs, public BigQuery datasets.
• Difficulty/time: Intermediate → 1 week.
• Outcomes: modular transformations, testing, DAG lineage, collaboration workflows.
• Extensions: add CI with dbt Cloud or GitHub Actions, automated docs.

7. Data catalog & lineage demo

• Objective: Build a mini data catalog and capture lineage for datasets.
• Tech: Amundsen/Apache Atlas/Marquez, dbt (to generate lineage), Postgres metadata store.
• Steps: deploy catalog locally, ingest metadata from dbt and Airflow, annotate datasets, search and view lineage, add data quality tags.
• Data: use datasets from previous projects.
• Difficulty/time: Intermediate → 4–7 days.
• Outcomes: metadata management, discoverability, governance basics.
• Extensions: integrate with SSO, policy enforcement.

8. Time-series pipeline & analytics (metrics store)

• Objective: Ingest time-series metrics, downsample, and serve for queries/alerts.
• Tech: InfluxDB/TimescaleDB, Telegraf or Kafka for ingestion, Grafana for visualization, Python for aggregation tasks.
• Steps: generate device/metric events, ingest into TSDB, write continuous aggregation/retention policies, build dashboards, set up simple alerting.
• Data: synthetic IoT or public telemetry.
• Difficulty/time: Intermediate → 3–5 days.
• Outcomes: time-series modeling, retention management, aggregation, visualization.
• Extensions: export metrics to Prometheus, integrate anomaly detection.

9. Build a small data warehouse with star schema + BI dashboard

• Objective: Design a dimensional model, ETL to populate fact/dim tables, and build dashboards.
• Tech: PostgreSQL/BigQuery/Snowflake, dbt for transformations, Metabase/Looker Studio/Power BI for BI.
• Steps: choose domain (e.g., e-commerce), model star schema, load data, compute slowly changing dimensions (SCD Type 2), create dashboards showing KPIs.
• Data: e-commerce datasets on Kaggle, synthetic data generator.
• Difficulty/time: Beginner→Intermediate → 3–7 days.
• Outcomes: dimensional modeling, SCDs, BI tooling, end-to-end analytics pipeline.
• Extensions: add role-based access control and row-level security.

10. End-to-end ML feature pipeline (feature store)

• Objective: Create reliable feature pipelines for ML training and serving.
• Tech: Feast or custom solution, Spark/pandas, Kafka for real-time features, feature store backed by Redis/BigQuery/S3.
• Steps: design features, implement batch feature computation, serve features for model training and online inference, ensure consistency and monitoring.
• Data: recommendation or user-event datasets.
• Difficulty/time: Advanced → 1–2 weeks.
• Outcomes: feature engineering at scale, online vs batch consistency, feature versioning.
• Extensions: integrate with an ML model serving endpoint and evaluate model drift.

General implementation tips

• Start small and iterate: build a minimal working pipeline, then add reliability, tests, monitoring, and scaling.
• Use Docker to containerize components so they’re reproducible.
• Version everything: code, infrastructure (IaC), dbt models, and schema migrations.
• Add observability early: logging, metrics, and alerts for job failures and data quality.
• Practice writing idempotent jobs and schema evolution handling.
• Learn through failures: create corrupt input files or duplicate events to exercise error-handling.

Quick learning path (recommended order)

1. Project 1 (ETL CSV → Postgres)
2. Project 5 (Airflow orchestration)
3. Project 3 (Spark batch)
4. Project 2 (incremental/CDC)
5. Project 6 (data lakehouse + dbt)
6. Project 4 (streaming Kafka)
7. Project 10 (feature store / ML pipeline)

If you want, I can:

• generate a concrete step-by-step starter repo or checklist for any one of these projects,
• pick a dataset and give you a full implementation plan with commands and sample code snippets,
• or recommend learning resources and tutorials matched to a chosen project.

Which project would you like to start with?