Are you ready to join our digital revolution journey?
At Aleph, we’re not just part of the digital advertising landscape—we’re shaping its future. Representing the world’s leading platforms, including TikTok, Amazon, Google, and nearly 55 others, we operate in 130+ markets across new and existing geographies.
Our mission is to empower advertisers and brands to unlock the full potential of these platforms' advertising capabilities. By fostering long-lasting partnerships, we create limitless opportunities for people and businesses to advertise effectively at both local and global levels.
With a presence spanning continents, Aleph offers you the chance to be part of a fast-growing, innovative team where your work makes a direct impact. If you’re ambitious, forward-thinking, and eager to thrive in a dynamic, global environment, Aleph is the perfect place to build your career.
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.
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- 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
