10 Essential Skills Your Team Needs to Build an Analytical Data Platform

Build an Analytical Data Platform

Building a modern analytical data platform is more than just choosing the right database or ETL tool. It requires a blend of business insight, data expertise, architecture design, and automation savvy. In this article, we’ll explore ten essential skills your team needs to design, develop, and maintain a robust, scalable, and high-value data platform.

1. Business Understanding

Before diving into any technical work, your team must understand the business domain and the data itself. This doesn’t mean every engineer needs to be a data analyst, but they should know:

• Which source systems hold the data (CRM, ERP, marketing platforms, etc.)
• Key business objects (customers, contracts, opportunities) and how they relate
• Business processes behind the data, like a customer’s lifecycle or sales funnel

By grounding the team in real-world outcomes—such as improving customer retention or reducing churn—engineers stay focused on delivering measurable ROI.

2. Objective Setting & ROI Focus

Clear objectives guide every stage of your platform’s development. Whether your goal is to accelerate financial reporting or enable real-time marketing analytics, defining the desired outcomes:

• Helps prioritize features and data sources
• Aligns stakeholders around common metrics
• Boosts motivation by tying work to tangible business value

Teams that regularly track ROI milestones can adjust scope and resources proactively, ensuring the platform grows in step with organizational goals.

3. Data Understanding & Modeling

A deep dive into your source systems reveals hundreds—even thousands—of tables. Your engineers need to know:

• Primary and foreign keys connecting entities
• Relationship cardinalities (one-to-one, one-to-many, many-to-many)
• Data quality quirks and domain-specific rules

This understanding informs the modeling approach—be it third normal form, star schemas, or Data Vault—ensuring downstream analytics are consistent and reliable.

4. Data Acquisition Techniques

Extracting data from source systems can take many forms:

• Full daily extracts via CSV or JSON files
• API calls for near-real-time data feeds
• Change Data Capture (CDC) for incremental updates

Knowing when to use each approach minimizes data latency, reduces load times, and optimizes storage. CDC, in particular, slashes the volume of data transferred, but requires robust handling to maintain consistency.

5. Structured Architecture: The Medallion Approach

Dumping raw data into a single database is a recipe for chaos. Instead, adopt a layered “medallion” architecture:

• Bronze Layer (Staging/Landing): Raw data as ingested
• Silver Layer (Cleansed, Integrated): Unified and harmonized data across systems
• Gold Layer (Presentation): Curated tables/views for business users and BI tools

This separation of concerns simplifies debugging, improves performance, and clarifies responsibilities for each team member.

6. Data Integration & Modeling in the Silver Layer

The silver layer is where the “magic” happens:

• Integrating disparate systems into a unified view
• Applying your chosen modeling technique (e.g., star schema, Data Vault)
• Ensuring referential integrity and consistent business definitions

Investing in a proven modeling framework not only scales with additional data sources but also enables automation and accelerates the onboarding of new subject areas.

7. Temporality & Historical Tracking

Beyond technical timestamps (extract load times), your data has business timelines:

• Contract start/end dates
• Customer sign-up and churn events
• Promotion or campaign effective periods

Implementing snapshot tables, slowly changing dimensions, or time-aware modeling ensures accurate trend analysis, historical comparisons, and auditability.

8. Code Generation & Automation Tools

Hand-coding every pipeline is time-consuming and error-prone. Leverage tools that:

• Automatically generate ETL/ELT code based on templates
• Orchestrate complex workflows and dependencies
• Enforce consistency through standard patterns and conventions

Automation not only speeds up development but also improves data quality by reducing manual interventions.

9. Agile Development & Traceability

Adopting an agile mindset means delivering small, working increments quickly. Apply traceability by:

• Defining clear targets (e.g., monthly revenue report)
• Mapping those targets back to specific source tables
• Focusing on data that directly supports your objectives

This approach prevents “scope creep” and ensures that every pipeline built serves an immediate analytical need.

10. DevOps & Cost Management

Once pipelines are automated, you need:

• Orchestration frameworks (e.g., Airflow, Dagster) to schedule and monitor jobs
• CI/CD for data code, including version control and automated testing
• Cost monitoring tools to track cloud resource usage and optimize performance

Effective DevOps practices guarantee reliability, while cost-awareness keeps your platform sustainable in the cloud era.

• Data Profiling: DataHub continuously monitors and analyzes data quality, automatically generating profiling metrics that reveal data distributions, identify anomalies, and help maintain high data quality standards. It provides key statistics such as row and column counts, query frequency, top users, and last update timestamps, along with detailed attribute profiling, including value ranges, central tendencies, null and distinct values. The table below shows some examples of these profiling metrics.

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Conclusion

Building an analytical data platform is a multifaceted endeavor. By equipping your team with these ten skills—spanning business understanding, data modeling, architecture design, automation, and DevOps—you’ll lay the foundation for a platform that delivers consistent insights, scales gracefully, and drives real business value.