Data and Analytics
QlikView, BI platforms, data engineering, and analytics talent for enterprise environments. Reporting and platform roles both.
What we cover.
Technologies and roles.
Technologies
Typical roles
- Senior
- Lead
- Senior
- Principal
- Mid-Senior
Where these roles live.
The typical environments where these roles sit — useful framing if you're scoping a new brief.
Data and analytics placements sit between two worlds — legacy enterprise BI (QlikView, Tableau, large Power BI estates) where reliability and reporting cadence matter, and modern data platforms (dbt, Snowflake, Databricks) where build-velocity does. We recruit for both. Most clients are either modernising an older reporting stack or scaling an analytics function past the "single data engineer" stage.
How we approach it.
Why Data & Analytics placements need a different approach from generic tech recruitment.
- Tool-honest
- QlikView and Tableau experience are real distinct skill sets, not interchangeable BI rebranding. We screen on the actual tool you need, not "BI generalist."
- Modelling depth, not just SQL
- For data engineering and analytics engineering, we screen on data-modelling decisions — star schemas, slowly-changing dimensions, dbt patterns — not just "writes SQL."
- Scale-honest
- A data engineer who's shipped pipelines processing TBs/day is a different hire from one who's done MBs/day. We brief and screen accordingly.
Recent placements.
Anonymised — we don't publish client names without written permission. Numbers are real.
Senior Data Engineer (dbt + Snowflake)
Joined a B2B SaaS scale-up's analytics platform team, standing up modern dbt-on-Snowflake infrastructure.
Analytics Lead
Placed into an industrial group's analytics function — leading 5 analysts across Power BI and Tableau estates.
QlikView Developer
Hired into a financial-services firm maintaining a large QlikView estate during phased Tableau migration.
Common questions.
Common questions about Data & Analytics placements. If yours isn't here, ask directly.
Do you cover QlikView, or only modern data tools?
Both. QlikView remains live at enterprise scale and we have real pipeline depth there. Modern stack (dbt, Snowflake, Databricks) is also strong, especially for scale-up analytics platforms.
What's the line between Data Engineer and Analytics Engineer for you?
Data Engineer = pipelines, ingestion, infrastructure (Spark, Airflow, streaming). Analytics Engineer = modelling layer (dbt), business-logic SQL, semantic-layer thinking. We screen for the right one based on your brief.
Do you handle data science roles?
Selectively. We're strongest on data engineering and analytics roles; ML engineering and applied data science we take on when the brief is concrete (specific model class, deployed in production, etc.) rather than open-ended "build our ML team."
Which regions are strongest for data talent?
Romania, DACH, and Poland are deepest. Israel for scale-up analytics engineers; Brazil for senior data engineers fluent in English for global teams.
Hiring for Data & Analytics? Let's talk.
Founder-led discovery. Reply within 24 hours — either with timing and a fit read, or with a polite redirect to who you actually need.