Data Engineer

We are seeking an experienced Data Engineer with a focus on building robust data systems to support digital advertising platforms. In this role, you will design and maintain scalable data pipelines, process large volumes of real-time and batch data, and support cross-functional teams in deriving insights from data. The ideal candidate has expertise in data engineering and a strong understanding of the tools necessary for handling complex, high-velocity datasets commonly found in the advertising ecosystem.

This position requires hands-on experience with cloud technologies, data frameworks, and real-time processing solutions to ensure data accuracy, scalability, and performance.

Required Skills & Experience:

• Bachelor’s or Master’s degree in Data Engineering, Computer Science, or related field.
• At least 5 years of experience in data engineering, including building and optimizing ETL processes.
• Strong proficiency in Python, Java, or Scala for data processing and pipeline development.
• Experience with distributed data processing frameworks such as Apache Spark, Apache Beam, or Hadoop.
• Expertise in real-time data processing using tools like Apache Kafka, AWS Kinesis, or Google Pub/Sub.
• Proficiency with cloud platforms like AWS (Redshift, S3, Glue), Google Cloud (BigQuery, Dataflow), or Azure (Data Lake, Synapse).
• Strong SQL and NoSQL skills for building and optimizing data stores (e.g., PostgreSQL, Cassandra, DynamoDB).
• Familiarity with containerization and orchestration tools such as Docker and Kubernetes.
• Experience automating data workflows with Airflow, Prefect, or similar orchestration tools.

Desired Skills & Experience:

• Experience managing and processing high-volume datasets in advertising, e-commerce, or similar domains.
• Knowledge of data lakes, data warehouses, and building architectures that support both structured and unstructured data.
• Familiarity with batch and real-time data integration techniques.
• Experience in implementing and maintaining CI/CD pipelines for data applications.
• Knowledge of machine learning pipelines and integrating models into data systems.
• Strong understanding of data security, governance, and compliance best practices.

What You Will Be Doing:

Tech Breakdown:

• 50% Building and Optimizing Data Pipelines
• 30% Real-Time Data Processing and Integration
• 20% Collaboration with Data Science and Engineering Teams

Daily Responsibilities:

• 60% Hands-On Data Engineering and Development
• 20% Building and Optimizing ETL Processes
• 20% Monitoring, Debugging, and Reporting on Data Systems