Data Platform Engineering: Applying Platform Principles to Data

Data engineering is often years behind software engineering in terms of operational maturity. Data Platform Engineering aims to bridge this gap by applying Platform Engineering principles to the data world.

The Problems in Traditional Data Orgs

• Fragile Pipelines: Manual deployments and lack of testing lead to frequent data quality issues.
• Data Silos: Every team builds its own infrastructure, leading to fragmented governance.
• Slow Delivery: Data scientists spend 80% of their time on infrastructure and data cleaning.

Pillars of a Data Platform

1. Self-Service Ingestion: Allow teams to onboard new data sources without opening tickets.
2. Data-as-Code: Manage transformations (SQL, Python) using Git, CI/CD, and peer reviews.
3. Automated Data Quality: Implement "data contracts" and automated testing to catch anomalies before they reach the data warehouse.
4. Governance by Design: Centralized access control, data masking, and lineage tracking.

Data golden paths (where the platform creates leverage)

As with a software platform, the goal is to make standard journeys simple and reliable:

• Ingest → transform → publish with one consistent template (repo, CI, conventions, observability)
• Data contracts: expected schemas, SLOs (freshness, completeness), quality rules
• Environments (dev/stage/prod) + controlled promotion of changes

These paved paths are what save time for data teams and reduce data quality incidents.

Day 2 matters: operate the data product

A data platform must also ship the “after”:

• alerting on freshness and drift (schema, volume)
• clear runbooks and ownership (who fixes what, in what timeframe)
• actionable lineage in incidents: “which dashboards depend on this dataset?”

Without this layer, the platform becomes an accelerator… for incidents.

What to ship first

Start with the highest-frequency journeys:

• onboard a new data source (connectors + access + observability)
• build a transformation with CI checks and data quality tests
• publish a dataset with clear ownership and documentation

Measuring outcomes

• time-to-first-pipeline
• data quality incident rate and MTTR
• % of pipelines using standard templates
• lineage coverage for critical datasets

Conclusion

A Data Platform is not just a collection of tools (like Airflow or Snowflake). It's a cohesive product that enables data teams to work with the same speed and reliability as software teams. By investing in Data Platform Engineering, you turn data from a bottleneck into a competitive advantage.