Analytics Engineer (f/m/d)

As an Analytics Engineer (f/m/d) at adjoe, your primary responsibilities will include conducting data analysis to identify significant improvements for our ad network, managing and creating data models and dashboards, and supporting online experiments like A/B testing. You will work with large datasets to extract relevant metrics and translate analytical needs into technical solutions, thus bridging the gap between analysis and engineering.

To be considered for the Analytics Engineer position at adjoe, applicants should have a background in data analysis, data engineering, or analytics engineering, with proficiency in SQL, Python, and experience working with Big Data frameworks. Familiarity with online experimentation practices and experience in building data pipelines and analytical workflows will also be valuable.

At adjoe, our Analytics Engineers utilize an advanced tech stack that includes tools like Apache Kafka for data bus systems, Python and SQL for analytics, and Airflow for workflow orchestration. We also use data processing technologies such as Presto and Spark to manage large datasets effectively, ensuring seamless operation and quick insights.

At adjoe, we believe in investing in your future. Our development program offers regular feedback and tailored support to help you grow your skills and reach your career goals. Whether it's through workshops or mentorship, you’ll have abundant opportunities to expand your expertise and advance within our company.

Working as an Analytics Engineer at adjoe means you enjoy a range of benefits, including flexible working hours, 30 vacation days, and access to our in-house gym. Additionally, we offer support for relocation to Hamburg, including visa assistance, making it easy for you to transition into your new role and home.

In my experience, successful data analysis in ad tech starts with understanding the core metrics that drive the business. I would begin by identifying relevant datasets, and then apply statistical techniques and models to extract insights. For instance, using SQL for querying data and Python for additional analysis allows me to comprehend trends and anomalies to optimize campaigns.

To ensure accurate A/B testing results, I prioritize using well-defined objectives and a robust experimental design. It's crucial to work collaboratively with cross-functional teams to develop test hypotheses. Furthermore, I frequently run sample size calculations before implementation, and I leverage tools to track key performance indicators during the test phase, all while ensuring data cleanliness and integrity.

I leverage techniques like regression analysis and machine learning algorithms to build predictive models. For example, using historical data to identify patterns, I can apply models in Python to forecast future outcomes. It’s essential to continuously validate and refine these models based on real-time data to maintain their accuracy.

Building and maintaining data pipelines is one of my key competencies. I have worked with technologies like Apache Airflow and Apache Kafka to automate data extraction, transformation, and loading processes. I'm meticulous about setting up monitoring systems to ensure the reliability and performance of these pipelines, which is critical for real-time applications in ad tech.

Handling large datasets requires an organized approach. I typically rely on distributed computing frameworks such as Spark for data processing to efficiently analyze large volumes. I also work on optimizing queries to reduce data load times and ensure I only access necessary information for analysis, thus improving performance further.

Dashboards are fundamental for visualizing key performance indicators and tracking real-time data metrics. They help me communicate findings effectively to stakeholders, and I usually develop interactive dashboards using tools like Tableau or Grafana, which allow users to explore data dimensions independently. This enhances decision-making processes across teams.

Certainly! In my previous role, I identified a trend in user engagement through extensive data analysis, which led to optimizing ad placements. This adjustment increased click-through rates by 25%, significantly boosting the campaign performance. My ability to translate analytical findings into actionable steps was crucial for this success.

Collaboration is key in analytics projects. I regularly engage in discussions with data scientists to align our models with business goals. With engineers, I ensure that analytical requirements are translated accurately into the technical specs needed for implementation. Weekly stand-ups and project meetings facilitate an ongoing exchange of ideas and troubleshooting.

I have substantial experience with online experimentation frameworks using A/B testing and multivariate testing approaches. Familiarizing myself with design principles for online experiments is essential. I enjoy contributing to the planning phases to create effective experiments that yield reliable data and insights for improving ad performance.

My passion for utilizing data to drive business strategies motivates me profoundly. The ad tech industry is continually evolving, which presents challenges that fuel my curiosity and problem-solving skills. Being at the forefront of technology enables me to engage with innovative solutions and see the direct impact of my work on helping businesses succeed.