Lead Data Engineer
Talcom
/job-details/4829
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Shiko punët aktive.
Lead Data Engineer
Location: Bitola, North Macedonia
Work setup: On-site with relocation support where needed
About the role
As Lead Data Engineer, you will be responsible for building and maintaining the data infrastructure that powers analytics and machine learning within PaymentIQ. You will ensure reliable data flow from source systems into analytics platforms and support both Data Analysts and Data Scientists.
The environment already includes ETL pipelines and OpenSearch-based analytics, but requires further development, optimization, and scalability.
Key responsibilities
- Design, build, and maintain ETL pipelines for large-scale transaction data
- Ensure data availability, quality, and performance across systems
- Optimize data storage and retrieval for analytics and machine learning use cases
- Support Data Scientists with data access and model integration
- Work on improving and scaling existing OpenSearch integrations
- Collaborate with Product and Engineering teams on data requirements
- Act as technical lead within the Bitola data team
- Guide best practices in data architecture and data handling
Requirements
- Around 5 years or more experience in data engineering
- Strong experience with ETL processes and data pipeline design
- Solid programming skills, preferably in Python or similar
- Experience with databases and large-scale data processing
- Understanding of data modeling and data architecture
- Experience working in production environments with high data volumes
- Ability to take ownership and structure work in a new team setup
- Excellent English skills
Nice to have
- Experience with OpenSearch, Elasticsearch, or similar tools
- Background in backend engineering or DevOps
- Experience with cloud environments and distributed systems
- Exposure to analytics tools such as Power BI, Tableau, or Snowflake
What you can expect
- Ownership of the data infrastructure for a new team and location
- Direct impact on analytics and machine learning capabilities
- Close collaboration with Data Science and Product
- Opportunity to help build and scale a data team from the ground up
Location: Bitola, North Macedonia
Work setup: On-site with relocation support where needed
About the role
As Lead Data Engineer, you will be responsible for building and maintaining the data infrastructure that powers analytics and machine learning within PaymentIQ. You will ensure reliable data flow from source systems into analytics platforms and support both Data Analysts and Data Scientists.
The environment already includes ETL pipelines and OpenSearch-based analytics, but requires further development, optimization, and scalability.
Key responsibilities
- Design, build, and maintain ETL pipelines for large-scale transaction data
- Ensure data availability, quality, and performance across systems
- Optimize data storage and retrieval for analytics and machine learning use cases
- Support Data Scientists with data access and model integration
- Work on improving and scaling existing OpenSearch integrations
- Collaborate with Product and Engineering teams on data requirements
- Act as technical lead within the Bitola data team
- Guide best practices in data architecture and data handling
Requirements
- Around 5 years or more experience in data engineering
- Strong experience with ETL processes and data pipeline design
- Solid programming skills, preferably in Python or similar
- Experience with databases and large-scale data processing
- Understanding of data modeling and data architecture
- Experience working in production environments with high data volumes
- Ability to take ownership and structure work in a new team setup
- Excellent English skills
Nice to have
- Experience with OpenSearch, Elasticsearch, or similar tools
- Background in backend engineering or DevOps
- Experience with cloud environments and distributed systems
- Exposure to analytics tools such as Power BI, Tableau, or Snowflake
What you can expect
- Ownership of the data infrastructure for a new team and location
- Direct impact on analytics and machine learning capabilities
- Close collaboration with Data Science and Product
- Opportunity to help build and scale a data team from the ground up
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