Plata
Cyprus
Engineering Manager - Golang (Telesales)
Programming Mid-Senior level 20h ago
- Golang
AI Summary
Manage and evolve the DWH Data Tools team and its critical data ecosystem tools. Lead people, planning, architecture, service maintenance, and stakeholder communication. Requires 1+ years managing engineering teams, strong data tools expertise, and B1+ English.
Key Highlights
Manage DWH Data Tools team and data ecosystem tools
Lead people, planning, architecture, and service maintenance
Strong Python, Node.js, and Go stack expertise
1+ years managing engineering teams
B1+ English for international collaboration
Key Responsibilities
Lead, mentor, and manage a team of engineers
Collaborate with product stakeholders and brainstorm technical solutions
Drive planning activities and manage development plans
Oversee service maintenance aligned with SRE processes
Oversee architecture, design, and implementation of data tools
Manage communication with data engineers, analysts, and stakeholders
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Technical Skills Required
Benefits & Perks
Relocation support to one of our hubs — Cyprus, Spain or Georgia — with assistance for the employee and their family
Flexible work from one of our offices or remote
Healthcare Coverage
Education Budget: Language lessons, professional training and certifications
Wellness Budget: Mental health and fitness activity reimbursements
Vacation Policy: Annual leave and paid sick leave as per applicable policies
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Nice to Have
Experience implementing or maintaining services alongside SRE processes
Work experience in DWH and/or the banking/fintech sphere
Familiarity with the technical stacks or internals of Superset, Jupyter Hub, or DataHub
Experience with data integration frameworks (e.g., Apache Kafka)
Knowledge of ETL tools (e.g., Airflow, DBT)
Experience with Kubernetes
Experience with Snowflake or Lake House architectures
Job Description
We are looking for an experienced and driven Engineering Manager to manage our DWH Data Tools team. This team is responsible for managing and evolving our critical data ecosystem tools, including Superset, Jupyter Hub, and DataHub, while also developing new, in-house solutions - including internal BI and data-exploration tools - to fit specific business needs.
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