Want to know if this job is worth applying to?
Rīga, Latvija
Onsite
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.
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.