Specialist, Data Intelligence & Analytics

Role purpose

The Specialist - Data Intelligence & Analytics works with digital advertising platform data, commercial

information, and BI datasets to turn raw information into reliable reporting and practical business insights. The

role combines hands-on database work with an understanding of digital marketing performance across

platforms, campaigns, advertisers, and markets.

This is an early-career role particularly suited to someone from a media, digital, or performance marketing

agency who has already worked directly with advertising data and databases, and wants to grow beyond

standard dashboard reporting. The specialist uses SQL and cloud-based analytical datasets, supports

analysts with dependable information, and learns selected BI data routines to provide continuity under

guidance. Enterprise data engineering and infrastructure remain with the responsible Data and IT teams.

Role scope

• Hands-on data work: Query, combine, validate, and prepare advertising-platform and business datasets

using SQL and cloud data environments such as BigQuery or comparable solutions.

• Reporting and insight: Support dashboards, recurring reporting, and focused analysis of digital

campaign, advertiser, platform, and commercial performance.

• BI workflow continuity: Help monitor and troubleshoot agreed data refreshes and connections, with

support from the BI team.

• Technical growth: Deepen SQL and cloud-data skills and develop toward automation, statistical analysis,

and more advanced analytical methods.

What you'll do:

Advertising data and business analysis

• Analyze performance: Work with digital advertising metrics such as spend, impressions, clicks,

conversions, campaign performance, and revenue to identify trends, anomalies, differences, and possible

drivers across platforms and markets.

• Connect context: Combine platform, CRM, financial, and internal operating data to support questions

across markets, advertisers, products, and teams.

• Explain results: Translate findings into concise observations and useful visualizations, clearly

distinguishing facts, assumptions, and data gaps.

Reporting, data preparation, and quality

• SQL and cloud data: Work directly with database tables using SQL to query, join, clean, and organize

datasets in Google BigQuery or comparable cloud data environments; help maintain straightforward

reporting views and transformations.

• Support BI reporting: Build and maintain reporting datasets, dashboards, and recurring reports using

agreed definitions in Looker Studio or comparable tools.

• Validate reliability: Check completeness, duplicates, consistency, unexpected changes, and reconciliation

against source totals; document and escalate issues.

Data connections and lightweight automation

• Follow the data: Understand how data from digital advertising platforms, CRM, and internal sources

reaches cloud data environments such as BigQuery and feeds reports; help check scheduled refreshes

and investigate missing or delayed data.

• Support connections: Assist colleagues with existing advertising-platform data connections and basic

API-related issues, such as changed fields or incomplete responses.

• Simplify repeatable work: Improve recurring data preparation and validation primarily through SQL;

experience with Python, another scripting or programming language, or practical automation tools is useful

but not central to the role.

• Provide continuity: Learn and document selected BI data refresh and preparation routines so they can be

supported when the main technical colleague is unavailable; escalate infrastructure issues to Data or IT

Collaboration and professional growth.

• Clarify requirements: Work with analysts and business colleagues to understand the question, expected

outputs, and suitable data sources.

• Document work: Keep data sources, logic, dependencies, procedures, and known limitations

understandable and reusable.

• Develop capabilities: Build on practical statistical understanding and gradually apply approaches such as

segmentation, anomaly detection, forecasting support, and introductory predictive analysis where they

improve business decisions.

What are we looking for:

Experience and education

• Relevant experience: 1+ year of hands-on experience in digital marketing analytics, performance

reporting, business intelligence, or data analysis involving direct work with databases. Experience gained

in agencies, relevant internships, or substantial practical projects is welcome.

• Preferred background: Experience in a media agency, performance marketing agency, digital agency,

ad-tech company, or advertising analytics team is a strong advantage, particularly if it involved working

directly with digital advertising platform datasets rather than only finished dashboards.

• Education: Degree or equivalent practical experience in analytics, statistics, mathematics, computer

science, engineering, economics, or a related field. A background in statistics or quantitative analysis is a

particular advantage.

Capabilities

• SQL - essential: Practical ability to query and combine structured data independently using SQL (e.g.,

joins, filters, aggregations, and basic transformations). Advanced SQL expertise is not expected, but

database work should be familiar.

• Cloud data platforms: Hands-on experience with Google BigQuery or a comparable cloud data

warehouse or analytics environment, such as Amazon Redshift, Snowflake, or Azure-based data solutions,

is highly valued. No specific cloud vendor experience is required

• Reporting and analysis: Can prepare datasets and work with reporting tools such as Looker Studio,

Power BI, Tableau, or Looker; understands campaign metrics and knows how to investigate inconsistent

results

• Database understanding: Understands database tables, relationships, refreshes, and the path from

source platforms into reporting datasets; has worked with underlying data rather than only ready-made

dashboards.

• Digital platform data: Experience working with advertising or measurement data from platforms such as

Google Ads, Meta Ads, TikTok Ads, Microsoft Advertising, Amazon Ads, DV360, or GA4 is a strong

advantage. Familiarity with one or several platforms is sufficient; expertise across all platforms is not

expected.

• Analytical and statistical thinking: Comfortable with data distributions, averages, variation, trends,

comparisons, and basic statistical reasoning; able to spot anomalies, challenge assumptions, and explain

results in business terms.

• Communication and ownership: Clear English, careful documentation, collaborative working style, and

willingness to learn from both business and technical colleagues

• Additional technical skills: Exposure to Python, JavaScript, Java, R, or another programming or scripting

language, as well as APIs, or basic automation, is a plus rather than a requirement. Practical curiosity and

willingness to learn are more important than a particular language.

Measures of success:

• Trustworthy output: Reports and datasets are accurate, consistent, traceable, and delivered on time
• Useful insight: Analysis identifies meaningful performance signals and supports practical business

decisions

• Operational resilience: Selected BI data connections and refreshes are monitored, documented, and

supported with appropriate escalation

• Efficiency: Manual preparation or recurring reporting effort is reduced through safe, maintainable

improvements

• Growth: Increasing independence with SQL, cloud-based datasets, advertising-platform data structures,

and selected BI workflows, with opportunities to develop toward advanced analytics.