Which data role should you hire first?

Hiring guidance

Start with the scope

Most startups err by hiring a data scientist first. This person typically expects clean datasets and functional pipelines. Without an engineer to build the plumbing, the scientist spends 80 percent of their time performing manual extraction, transformation, and loading tasks. Data quality issues remain buried until the scientist encounters them during modeling, which halts progress. Your first hire should solve the pipeline bottleneck .

A data engineer manages the ingestion, storage, and transformation lifecycle. They treat data as a product by ensuring warehouse reliability and schema integrity. If you prioritize building an automated source of truth, hire an engineer. If you prioritize interpreting existing metrics for stakeholders, hire a BI analyst. Combining these roles into one title causes failure. Engineers focus on uptime and architectural scalability, while analysts bridge the gap between technical datasets and executive decision-making. Distinguishing these needs early saves you from churn and wasted compute costs.

Prioritize technical infrastructure before complex experimentation. An early engineer creates the foundation for future warehouse modeling and automated reporting. Adding a BI analyst comes later to convert that stored data into accessible dashboards. Hiring them in the wrong order forces technical staff to handle manual reporting or analytical staff to maintain brittle pipelines. Both scenarios lead to frustration and high turnover.

Hiring guidance

Pipeline Tool Proficiency

Verify experience with specific orchestration frameworks like Airflow or Prefect rather than generic ETL knowledge. Ask the candidate to explain how they handle backfilling data when a partition fails. Strong candidates demonstrate a clear process for re-running workflows without duplicating records. Weak candidates describe manual scripts that lack error logging or automatic retries.

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Data Warehouse Modeling

Assess the ability to structure a star schema within a platform like Snowflake or BigQuery. The candidate should justify why they denormalize certain tables for read efficiency. A reliable engineer avoids excessive joins that slow down query performance. They explain the difference between staging layers and final presentation layers in a data warehouse.

Hiring guidance

SQL Optimization

Test how they diagnose a slow query. A skilled engineer examines the execution plan and suggests partitioning or clustering strategies to reduce scan volume. They show evidence of managing costs by limiting data retrieval. An unqualified applicant focuses only on writing basic select statements without considering the impact on warehouse compute credits.

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Version Control for Data

Require evidence of using Git for dbt models or SQL transformations. Data changes frequently, so tracking historical changes to business logic is vital. An experienced professional keeps documentation in the codebase. Someone who treats SQL files as disposable assets on a local drive will create long-term technical debt.

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Evidence to bring into the hiring conversation

Look for individuals currently working in small teams who manage the entire data lifecycle. These people have experience moving data from raw sources to production-ready tables without relying on massive platform support teams. Their motivation stems from building clean systems that solve actual business problems rather than just moving rows around.

Target backgrounds involving cloud infrastructure management where the candidate demonstrated ownership of automated ingestion services. They often gravitate toward environments where they can choose the stack and define the warehouse architecture. Their interest usually centers on efficiency, cost reduction, and creating systems that function without daily manual intervention or constant debugging.

Hiring guidance

Problems worth catching early

Hiring a candidate with strong research experience but zero exposure to pipeline maintenance or production code. This person will struggle to set up basic warehouse schemas and will likely neglect necessary monitoring tasks.

Over-indexing on specific certification names without requiring a code review of their previous warehouse projects. Theoretical knowledge of a platform does not equate to the ability to debug failed ingestion jobs at 2 AM.

Assuming an analyst can perform the engineering work. Analysts prioritize rapid iteration on reports, which often leads to poor naming conventions and unoptimized pipelines that break under increased volume.

Hiring guidance

Decisions to make before the search

Initiate the search by defining whether your data is currently siloed or already residing in a warehouse. This determines if you need a builder who manages source integration or a refiner who manages existing models.

Perform a live screen focused on one concrete pipeline challenge. Give them a raw API response and ask them to map it into a functional warehouse table structure using basic logic.

Evaluate their final shortlist by asking how they handle technical debt. A strong candidate should describe a balance between rapid delivery and the necessity of refactoring code for long-term sustainability.

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