Data Analytics Manager

About the role

The role translates raw media and marketing data into scalable Power BI solutions for global brands. It links data engineering foundations to business decisions through dashboards and data models, supporting strategy and governance within Data Infra & Ops.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Interactive dashboards visualize marketing and media requirements, delivering clear, actionable insights in Power BI.

Data models are built and maintained using DAX, optimized for performance, and aligned with business rules and logic in Power BI.

Project outlines, estimations, and progress updates are managed using Wrike to support the Project Manager.

Data quality and governance practices are monitored and enforced, with issues resolved to ensure accuracy and integrity.

Findings and insights are presented clearly, and end-users receive training on effective use of BI tools and dashboards.

Consultancy is provided on how to construct and interpret data solutions for clients and internal partners.

Requirements

A completed Bachelor's or Master's degree in Data Analytics, Computer Science, Business Administration, or a related field is required.

3 to 5 years of experience in a similar BI Developer or Data Analyst role is required.

Strong, proven experience with Power BI, DAX, and SQL is required.

Experience with cloud data warehouses such as Snowflake and data integration tools such as Adverity is required.

Knowledge of the marketing and media landscape is a major plus.

Excellent communication skills, the courage to challenge stakeholders, and the ability to manage expectations are required.

Nice to have

Experience with cloud data warehouses and data integration tools is valued.

Knowledge of the marketing and media landscape is a major plus.

Practical notes

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data analytics roles bridge business needs with technical data platforms using tools such as Power BI, DAX, and SQL.

Cloud data warehouses and ETL processes support modern media and marketing analytics.

Agile delivery and continuous improvement are common in data infrastructure and operations teams.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.