Talent Strand

Data and Analytics recruitment

You need people who can turn raw data into reports, dashboards and forecasts your teams will actually use. Talent Strand fills data and analytics roles on your payroll, from entry level analysts to senior data scientists.

Hiring managers compete for candidates who know SQL, Python and at least one BI tool such as Tableau or Power BI. Data analysts and BI developers often come from operations or finance teams. Data engineers bring pipeline and warehousing skills. Data scientists add statistics and machine learning. Many data professionals want a clear path from cleaning data to influencing decisions, and they will ask how your company uses its data before they accept an offer.

Hiring goes wrong when companies write a list of every tool they own and expect one person to know all of them. A job post that asks for Python, R, Scala, AWS, Azure, Snowflake, dbt, Tableau and Power BI narrows the pool to people who have touched each tool instead of people who can solve your actual reporting problem. Focus on the first problem the hire must solve. Another common miss is an interview loop that tests coding tricks instead of asking the candidate to walk through a real dataset.

Titles we recruit

  • Data Analyst
  • Data Engineer
  • BI Developer
  • Data Scientist
  • Senior Data Analyst
  • Senior Data Engineer
  • Senior BI Developer
  • Senior Data Scientist

How candidates are assessed

Candidates for data and analytics roles are assessed through practical work. The work sample matters. We ask for a SQL exercise that requires joins, aggregations and handling missing values. For data engineer roles, the candidate may also complete a small Python task to read a file, transform it and load it into a table. BI developer candidates walk through how they would build a dashboard from a sample dataset and explain the choices behind filters, drill downs and refresh schedules. Data scientist candidates present a past model and explain the business decision it supported. Reference checks focus on how the person worked with stakeholders to define metrics and clean up messy source data.

Where we recruit for these roles

Northeast, Midwest, South, West, and every state in between. Popular cities include New York City, Los Angeles, Chicago, Dallas, Fort Worth, Houston, San Antonio, Austin.

Questions employers ask

Which data roles should my company hire first?

Start with the role tied to your most urgent business question. A company that needs reliable dashboards usually hires a BI developer or data analyst. If your data sits in many systems and needs cleaning before anyone can use it, hire a data engineer first. The hire should spend most of the first quarter getting one dataset into shape rather than touching everything at once.

How do you test SQL and Python skills without turning the interview into a coding contest?

We use short, realistic tasks that mirror the work your team does. A SQL candidate might answer questions from a sample orders table. A Python candidate might clean a small CSV file. The goal is to see how the person thinks about data, rather than whether they memorized functions.

Do data scientists need to know Python, or is SQL enough?

Most data scientists in U.S. employers work with Python for modeling and automation. SQL remains the common language for pulling data from warehouses. We look for candidates who can use both, and we focus on how they apply those skills to a business problem rather than how many packages they can list.

What if we use a less common BI tool?

The underlying skills matter more than the specific tool. A BI developer who knows Tableau or Power BI can usually learn Looker, Qlik or another platform within a few weeks. We confirm learning speed during the interview by asking the candidate to explain how they would build a metric in the tool they know, then map that to yours.

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