Big Data Architect
Big Data Architects design the platforms that ingest, store, process, and serve massive datasets — distributed storage, streaming, batch processing, lakehouse architecture, performance and cost optimization. The work tends to mix architecture decisions, vendor selection, and the steady politics of platform choices.
What it's like to be a Big Data Architect
Most days mix architecture work, technology evaluation, and stakeholder conversations — designing data platforms, evaluating tools (Snowflake, Databricks, BigQuery, Redshift, Hadoop ecosystem, Kafka, Spark), reviewing schema and pipeline designs, and partnering with data engineering, analytics, and ML teams. You're often working at companies with substantial data scale, and the platform maturity shapes daily work — greenfield vs migration vs operate-and-improve.
What tends to be harder than people expect is the cost-and-politics dimension of platform decisions. Cloud spend can balloon quickly, vendor switching costs are real, and organizational silos make some integrations painful. Streaming vs batch, lakehouse vs warehouse debates have evolved with the tools, and regulatory data requirements (PII, GDPR, HIPAA) add complexity.
People who tend to thrive here are conceptual thinkers, comfortable with trade-offs, fluent in distributed systems, and patient with consensus-building. If you want hands-on coding all day, the architect seat steps back. If you like shaping data infrastructure that powers analytics and ML at scale, the role offers durable demand and significant strategic influence.
Where this role sits in the broader career landscape — and where it can take you.
Roles like this one sit within a broader occupational category. The numbers below reflect that full landscape — helpful for context, but your specific experience will depend on level, specialty, and where you work.
How this category is changing
Skills & Requirements
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