Data Engineer

• Use Dataform to build medallion data architecture while optimizing BigQuery and ClickHouse models
• Develop batch and real-time data pipelines on our GCP platform
• Orchestrate Airflow DAG pipelines for data imports and exports, with monitoring and alerting that safeguard data integrity
• Expose data to other teams through well-designed APIs, supported by CI/CD pipelines for automated deployments
• Uphold GDPR/CCPA compliance and maintain data governance standards across platforms

• Design, build, and optimize ETL/ELT pipelines that transform customer and internal data into actionable datasets
• Partner with Markets and Product teams to develop reporting tools and dashboards that guide business strategy and serve customer needs
• Build and maintain data lakes and feature stores used by ML Engineering team to develop ML solutions for subrogation

Senior Data Engineer

• Build scalable, high-performance data infrastructure that transforms complex transactional and financial data into trusted datasets for regulatory reporting and business operations.
• Develop reliable data pipelines and data products that power regulatory reporting, financial operations, analytics, and operational insights while ensuring data accuracy and reliability.
• Design, build, and optimize modern data models and distributed data pipelines.
• Leverage AI throughout the software development lifecycle to improve engineering productivity, strengthen data quality, and build trusted data products.
• Collaborate with cross-functional stakeholders to translate complex business and regulatory requirements into scalable, analytics-ready data models and solutions.
• Mentor Data Engineering teammates by sharing best practices, providing technical guidance, and helping raise engineering standards across the team.
• Collaborate in an agile environment to deliver scalable data solutions that support evolving business and regulatory needs.

• Design, develop, and maintain scalable data pipelines and data platforms
• Build and optimize ETL/ELT processes for large-scale data processing
• Work with Databricks to develop and manage data workflows and analytics solutions
• Implement data processing solutions using Apache Spark and modern data engineering practices
• Collaborate with data scientists, analysts, and business stakeholders to enable data-driven insights
• Ensure data quality, reliability, and performance of data pipelines
• Contribute to architecture decisions and data platform improvements
• Mentor junior engineers and promote best practices in data engineering

Lead Data Engineer, Snowflake

• Writing and improving data pipelines which load data in our data lake and transform it to the Feature store
• Ensuring all data is available in the right format and reliable quality. Working on improvements of the quality of data as well as challenges as data shift and concept drift
• Testing Data pipelines at staging environments and deploying them at production
• Writing proprietary packages / frameworks to be used for internal purposes to make the standard tasks easier (such as data load, model testing, exploratory data analysis, etc.)
• In collaboration with ML Engineers and Data Scientists taking part in A/B testing and monitoring solutions

Junior Data Engineer

• Designing, building, optimising, and troubleshooting scalable batch and streaming data pipelines where applicable.
• Contributing production-grade Python and SQL code aligned to engineering standards for maintainability, readability, observability, and operational resilience.
• Supporting safe and pragmatic modernisation across legacy Pentaho and modern Databricks platform capabilities.
• Improving Spark workload efficiency, query performance, orchestration reliability, storage optimisation, and platform scalability.
• Contributing to technical investigations, troubleshooting, and remediation planning for production issues and systemic engineering weaknesses.
• Implementing robust testing approaches including unit, integration, reconciliation, and data quality validation.
• Improving supportability and resilience across the data estate through monitoring, dashboards, alerting, runbooks, and operational tooling.
• Helping improve developer effectiveness through reusable tooling, templates, frameworks, and automation.
• Supporting data-heavy and AI-enabled product initiatives including recommendation systems, behavioural analytics, propensity modelling, and intelligent automation features.
• Collaborating with Data Engineers, Platform Engineering, SRE, Architecture, Governance, and Product teams to unblock delivery and support complex engineering challenges.

• You’ll lead complex engineering work across critical data pipelines and platform capabilities, acting as the technical quality bar for data engineering across the Tribe.
• This will include:
• - Leading and implementing complex engineering work across critical data pipelines and platform capabilities.
• - Taking the lead on difficult production incidents, performance bottlenecks, and data integrity issues.
• - Driving root-cause analysis and remediation for systemic engineering and operational weaknesses.
• - Designing, building, optimising, and troubleshooting scalable data pipelines across batch and streaming workloads where applicable.
• - Contributing production-grade Python and SQL code aligned to engineering standards for maintainability, readability, observability, and operational resilience.
• - Leading optimisation and stabilisation efforts across Pentaho ETL and Databricks lakehouse ecosystems.
• - Improving Spark workload efficiency, cluster utilisation, query performance, storage optimisation, orchestration reliability, and platform scalability.
• - Defining and implementing observability standards for critical pipelines and datasets.
• - Improving operational readiness across the data estate through monitoring, alerting, dashboards, runbooks, ownership clarity, and operational tooling.
• - Improving developer experience through reusable frameworks, CI/CD patterns, templates, libraries, and “golden path” implementation examples.
• - Promoting pragmatic and safe adoption of AI-assisted and agentic engineering approaches where they improve engineering outcomes.
• - Supporting data-heavy and AI-enabled product initiatives including recommendation engines, behavioural scoring, propensity modelling, and intelligent automation features.
• - Designing and building production services, APIs, and integration layers that expose new data capabilities and features into products and operational workflows.
• - Developing squad engineering leads through mentorship, coaching, technical guidance, and hands-on support.
• - Collaborating with Data Engineers, Platform Engineering, SRE, Architecture, Governance, Product teams and engineering leadership to unblock delivery and support complex engineering challenges.

Data Engineer jobs in Bulgaria

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Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Lead Data Engineer, Snowflake

Junior Data Engineer

Senior Data Engineer

Principal Data Engineer