Build the Data Platform That Powers Your Business

What is Data Platform Engineering?

Data platform engineering is the discipline of designing, building, and operating the infrastructure that enables organizations to collect, store, process, and analyze data at scale. It encompasses everything from data architecture and pipeline development to governance, quality monitoring, and the tooling that makes data accessible to business users, analysts, and data scientists.

The goal of data platform engineering is to create a reliable, scalable foundation that transforms raw data into business value. This means building systems that can handle growing data volumes, ensure data quality and governance, and enable both operational analytics and advanced AI/ML use cases.

Data platform engineering has evolved significantly over the past decade. The shift from on-premise data warehouses to cloud-native architectures, combined with the emergence of the modern data stack, has fundamentally changed how organizations approach data infrastructure.

Why Data Platform Engineering Matters

In the data-driven economy, the ability to turn data into insights quickly and reliably is a competitive advantage. Organizations with strong data platforms can:

• Make faster, better decisions: Self-service analytics and trusted data enable business users to answer questions in hours, not weeks.
• Power AI/ML initiatives: Machine learning models require clean, consistent data. A strong data platform is the foundation for successful AI.
• Scale efficiently: Cloud-native platforms scale automatically with data volumes, avoiding costly infrastructure upgrades.
• Ensure compliance: Data governance, lineage, and access controls support regulatory requirements like GDPR, HIPAA, and SOC2.

The Modern Data Stack

The modern data stack is a collection of cloud-native tools that work together to handle the complete data lifecycle. Unlike monolithic enterprise data platforms of the past, the modern data stack is modular, scalable, and optimized for SQL-based analytics and self-service.

Key characteristics of the modern data stack include:

• Cloud-first: Built on cloud infrastructure (AWS, Azure, GCP) with pay-as-you-go pricing and unlimited scalability.
• Modular: Best-of-breed tools for each function (ingestion, transformation, storage, analytics) that integrate through standard interfaces.
• SQL-centric: SQL remains the lingua franca for data analysis, making data accessible to a broader audience.
• ELT over ETL: Extract-Load-Transform patterns leverage the power of cloud data warehouses for transformation, simplifying pipeline architecture.

Core Components of the Modern Data Stack

A typical modern data stack includes:

• Cloud Data Warehouse: Snowflake, Databricks, or Google BigQuery provide scalable storage and compute for analytics workloads.
• Data Ingestion: Tools like Fivetran, Airbyte, or Stitch extract data from source systems and load it into the warehouse.
• Transformation: dbt (data build tool) transforms raw data into business-ready models using SQL and software engineering practices.
• Orchestration: Airflow, Dagster, or Prefect schedule and monitor data pipelines, ensuring data arrives on time.
• Business Intelligence: Looker, Tableau, or Power BI visualize data and enable self-service analytics.
• Data Catalog: Tools like Atlan, Alation, or DataHub provide discovery, documentation, and governance capabilities.

Data Lakehouse Architecture

The data lakehouse is an architectural pattern that combines the best features of data lakes and data warehouses. It addresses the limitations of both approaches, providing a unified platform for all data workloads.

Traditional data warehouses excel at structured data, ACID transactions, and SQL analytics but are expensive and limited in handling semi-structured or unstructured data. Data lakes offer cheap storage for all data types but lack governance, performance, and the reliability of data warehouses.

Data lakehouses, built on open table formats like Delta Lake, Apache Iceberg, or Apache Hudi, provide:

• ACID transactions: Reliable updates, deletes, and merges on data lake storage.
• Schema enforcement: Data quality through schema validation and evolution.
• Time travel: Query data as of any point in time for auditing and debugging.
• Unified batch and streaming: Same tables support both batch and real-time data.
• Open formats: Data stored in open formats (Parquet) accessible by multiple engines.

Medallion Architecture

A common pattern for organizing data in a lakehouse is the medallion architecture, which organizes data into three layers:

• Bronze (Raw): Raw data as extracted from sources, preserving full fidelity for reprocessing.
• Silver (Cleansed): Cleansed, validated data with consistent schemas and basic transformations.
• Gold (Business): Aggregated, business-ready data models optimized for analytics and reporting.

Data Pipeline Engineering

Data pipelines are the arteries of any data platform, moving data from source systems through transformation and into analytics-ready formats. Production-grade pipelines must be reliable, scalable, observable, and maintainable.

ETL vs ELT

The modern data stack has shifted from ETL (Extract-Transform-Load) to ELT (Extract-Load-Transform). In ELT, raw data is loaded directly into the data warehouse, and transformations happen using the warehouse's compute power. This simplifies pipeline architecture and leverages the scalability of cloud warehouses.

Pipeline Design Principles

• Idempotency: Pipelines can be safely re-run without creating duplicates or corrupting data.
• Incremental processing: Only process new or changed data rather than full reloads, improving efficiency.
• Data quality checks: Validate data at each stage, catching issues before they propagate downstream.
• Observability: Logging, metrics, and alerting provide visibility into pipeline health and performance.
• Version control: Pipeline code in Git enables collaboration, review, and rollback capabilities.

Orchestration

Orchestration tools like Apache Airflow, Dagster, and Prefect manage pipeline execution. They handle scheduling, dependencies, retries, and monitoring. Modern orchestrators treat pipelines as code, enabling software engineering practices like testing and CI/CD.

Data Modeling with dbt

dbt (data build tool) has become the industry standard for data transformation in the modern data stack. It enables analytics engineers to transform raw data into business-ready models using SQL, while applying software engineering best practices.

Why dbt Matters

Before dbt, data transformation was often a mix of stored procedures, Python scripts, and ad-hoc SQL. dbt brings structure and discipline:

• Version control: All transformations are SQL files in Git, enabling collaboration and change tracking.
• Testing: Built-in and custom tests validate data quality automatically.
• Documentation: Auto-generated documentation keeps data models understandable.
• Modularity: Reusable macros and packages avoid duplication.
• Lineage: Automatic dependency tracking shows how data flows through models.

Analytics Engineering

dbt has given rise to the role of analytics engineer—a hybrid between data engineer and data analyst. Analytics engineers focus on transforming raw data into clean, documented, tested datasets that business users can trust. They bridge the gap between data engineering infrastructure and business analytics needs.

Data Governance and Quality

Data governance ensures that data is accurate, secure, and properly used. As data platforms grow in complexity and importance, governance becomes critical for maintaining trust and compliance.

Data Catalog

A data catalog provides a searchable inventory of data assets. It enables data discovery, documents data meaning and context, and tracks who owns what. Modern catalogs like Atlan, Alation, and DataHub integrate with the data stack to automatically capture metadata.

Data Lineage

Lineage tracks how data flows from source to consumption. It answers questions like: Where does this metric come from? What happens if I change this table? Who will be impacted by this data issue? Lineage is essential for impact analysis, debugging, and compliance.

Data Quality Monitoring

Data quality issues can erode trust and lead to bad decisions. Modern data quality tools monitor for:

• Freshness: Is data arriving on time?
• Volume: Are row counts within expected ranges?
• Schema: Have columns changed unexpectedly?
• Distribution: Are values within expected patterns?

Tools like Monte Carlo, Great Expectations, and Soda provide automated monitoring and alerting for data quality issues.

Real-Time Data and Streaming

While batch processing remains the foundation of most analytics, many use cases require real-time or near-real-time data. Streaming architectures enable sub-second data latency for operational analytics, personalization, fraud detection, and event-driven applications.

Event Streaming with Kafka

Apache Kafka (and its managed alternatives like Confluent Cloud, Amazon MSK, and Azure Event Hubs) is the backbone of most streaming architectures. Kafka provides durable, scalable event streaming that decouples data producers from consumers.

Change Data Capture (CDC)

CDC captures database changes in real-time, enabling streaming replication from operational databases to analytics systems. Tools like Debezium and Fivetran capture inserts, updates, and deletes as events, eliminating the need for expensive full table scans.

Stream Processing

Stream processing engines like Apache Flink, Spark Streaming, and ksqlDB enable real-time transformations, aggregations, and joins on streaming data. This powers use cases like real-time dashboards, anomaly detection, and operational analytics.

Building a Data Platform Team

Successful data platforms require the right team structure and skills. The data organization has evolved beyond traditional roles, with new specializations emerging to handle the complexity of modern data infrastructure.

Key Roles

• Data Engineer: Builds and maintains data pipelines, infrastructure, and platform capabilities.
• Analytics Engineer: Transforms raw data into business-ready models using dbt and SQL.
• Data Analyst: Answers business questions, builds dashboards, and supports decision-making.
• Data Scientist: Builds ML models and advanced analytics on top of the platform.
• Data Architect: Designs overall data architecture and guides technology decisions.
• Data Product Manager: Owns the data platform roadmap and prioritizes based on business value.

Team Structures

Organizations structure data teams in various ways: centralized platform teams, embedded data engineers within product teams, or federated models with domain teams owning their data products. The right structure depends on organization size, data maturity, and how data is used across the business.

Why Salt for Data Platform Engineering?

Salt brings deep expertise in data platform engineering and the practical experience to make your data initiative successful. Here's what sets us apart:

Modern Data Stack Expertise: Our engineers have built data platforms on Snowflake, Databricks, BigQuery, and the full modern data stack. We bring hands-on experience with dbt, Airflow, Kafka, and the tools that power successful data organizations.

End-to-End Capability: From architecture to pipelines to governance, we handle the complete data platform lifecycle. Our Data & AI Pods provide dedicated teams that own your data platform delivery.

Business Value Focus: We don't build technology for technology's sake. Every engagement starts with understanding your business goals and designing the data platform to deliver measurable outcomes.

Pragmatic Approach: We recommend the right tools for your context, not the newest or most complex options. Start with quick wins, iterate based on feedback, and expand capabilities as value is demonstrated.

SPARK™ Framework: Our SPARK™ framework brings structure to data platform initiatives. Clear phases, quality gates, and transparent communication ensure predictable delivery and stakeholder alignment.

Knowledge Transfer: We build your team's capability alongside the platform. Documentation, training, and embedded collaboration ensure your organization can own and evolve the data platform independently.

Ready to modernize your data infrastructure? Schedule a data platform assessment with our team to discuss your goals and how Salt can help you build the data platform that drives business value.