Data Engineer

Key Responsibilities

• Design, build, and maintain data pipelines supporting automated campaign management and monitoring.
• Enable data load management and forecasting by processing and aggregating campaign-related metrics.
• Develop and maintain data models for campaign status, rollout progress, and batch-level insights.
• Ensure data quality, consistency, and reliability across integrated systems.
• Collaborate with backend and frontend teams to expose analytics-ready datasets and APIs.
• Support near-real-time monitoring and reporting of campaign execution and system behavior.
• Contribute to data observability, documentation, and continuous improvement of data workflows.

Technical Expertise

• Solid experience with data engineering fundamentals and pipeline development.
• Proficiency in SQL and working with structured and semi-structured data.
• Experience with cloud-based data platforms and managed data services.
• Familiarity with batch and streaming data processing concepts.
• Experience integrating data from distributed systems and APIs.
• Working knowledge of data validation, monitoring, and performance optimization.

Competencies & Skills

• Strong analytical mindset with the ability to understand complex, domain-driven data flows.
• Ability to translate business and technical requirements into scalable data solutions.
• Structured approach to data modeling and pipeline design.
• Understanding of how data supports automation, monitoring, and decision-making.
• Capability to work independently on defined tasks while aligning with the overall data architecture.
• Knowledge of data visualization and BI tools.

Soft Skills

• Clear communication with engineers, product managers, and stakeholders.
• Collaborative, team-oriented working style.
• Ownership mentality for data products and pipelines.
• Curiosity and willingness to continuously improve data solutions.
• Reliability and attention to detail in data-critical environments.

Nice to Have

• Experience with analytics or monitoring use cases in complex platforms.
• Exposure to campaign, rollout, or deployment data.
• Familiarity with event-driven or near-real-time data architectures.
• Interest in large-scale automation, platforms, or data-driven operations.