Data Engineer

Job Title: Data Engineer

Reports to: BI Manager

Location: Belgrade - Hybrid

The Role

The Data Engineer is responsible for designing, building, and maintaining scalable data pipelines and data platforms that support both batch (ELT) and real-time data processing. The role ensures reliable and efficient extraction, transformation, and loading of data from a wide range of internal and external sources to support business intelligence and analytics.

Working within a cloud-based data environment built primarily on
Amazon Web Services
, the Data Engineer will develop and maintain data infrastructure that enables the organization to make data-driven decisions. The solutions delivered will support analytical insights across the business, particularly in areas such as customer acquisition, operational performance, and analytics related to sports betting activities.

The role requires close collaboration with data architects, product teams, analysts, IT teams, and business stakeholders from project inception through to delivery.

Key Responsibilities – Engineering & Delivery:

• Design, develop, and maintain scalable batch and real-time data pipelines.
• Build ELT workflows to extract, transform, and load large datasets from multiple internal and external sources.
• Develop and maintain cloud-based data infrastructure using services such as
  Amazon S3
  ,
  AWS Glue
  ,
  AWS Lambda
  , and
  Amazon Redshift
  .
• Implement orchestration and scheduling workflows using
  Apache Airflow
  or
  Amazon MWAA
  .
• Develop data transformations and modelling processes using SQL and
  dbt
  .
• Integrate streaming and large-scale processing solutions using technologies such as
  Apache Kafka
  and
  Apache Spark
  .
• Ensure the reliability, performance, and scalability of production data pipelines and infrastructure.
• Participate in a scheduled on-call rotation to monitor, troubleshoot, and resolve high-priority data pipeline issues, ensuring the reliability of the data platform during weekends and bank holidays.
• Support the integration of data into enterprise data warehouses such as
  Snowflake
  or
  Redshift.

Security, Compliance & Governance:

• Implement secure data handling practices within cloud-based data platforms.
• Support the management of data access, permissions, and role-based controls across data systems.
• Ensure data pipelines and storage systems comply with internal governance policies and regulatory requirements.
• Maintain high standards of data quality, integrity, and traceability.
• Contribute to the implementation of data governance frameworks and best practices.

Agile & Collaboration:

• Collaborate with data architects to design and evolve scalable data architecture.
• Work closely with product teams, analysts, and business stakeholders to translate business requirements into technical data solutions.
• Partner with IT and infrastructure teams to support system integration and platform stability.
• Participate in agile development practices including sprint planning, reviews, and technical discussions.
• Communicate effectively with both technical and non-technical stakeholders.

Continuous Improvement & Innovation:

• Identify and implement opportunities to automate manual data processes.
• Continuously improve pipeline reliability, monitoring, and operational efficiency.
• Redesign and optimize data infrastructure to support increasing data volumes and evolving business requirements.
• Evaluate and introduce new tools, technologies, and best practices to enhance the data platform.
• Promote a culture of innovation and continuous improvement within the data engineering team.

The Person – Experience:

• Minimum of
  3 years’ professional experience
  in data engineering or a related technical role.
• Hands-on experience working with cloud data platforms, particularly
  Amazon Web Services
  .
• Strong experience working with large-scale production databases and SQL-based data warehouses such as
  Amazon Redshift
  ,
  Snowflake
  , or
  Google BigQuery
  .
• Proven experience developing ELT pipelines using
  dbt
  ,
  Apache Airflow
  , and Python.
• Familiarity with big data and streaming technologies such as
  Apache NiFi
  ,
  Apache Kafka
  , and
  Apache Spark
  .
• Experience with containerization and orchestration technologies including
  Docker
  and
  Kubernetes
  .

Skills & Attributes:

• Strong SQL and data modelling capabilities.
• Proficiency in Python for data pipeline development.
• Strong analytical and problem-solving skills.
• Ability to work with complex and large-scale datasets in production environments.
• A proactive and innovative mindset with a focus on continuous improvement.
• Ability to adapt quickly in a fast-paced and evolving technical environment.
• Strong communication and collaboration skills.
• Excellent English communication skills with ability to convey complex technical concepts to international teams.
• Willingness to contribute ideas, share knowledge, and participate in technical discussions.