شرح موقعیت
Role Summary: At bluBank, we are looking for an experienced Data Analytics Engineering Team Lead to lead the development of our analytical data foundation, ensuring that banking data is transformed into reliable, scalable, and well-governed assets for analytics, reporting, and decision-making. This is a hands-on technical leadership role combining team leadership, analytical data architecture, engineering excellence, and cross-functional collaboration. You will lead the Analytics Engineering team, establish technical standards and best practices, and oversee the development of high-performance data models, automated workflows, and trusted business metrics. Key Required Preferred
مسئولیتها
- Team Leadership & Development: Lead, mentor, and develop Analytics Engineers. Establish clear ownership, provide technical guidance, conduct code reviews, and foster a culture of accountability, collaboration, and continuous improvement.
- Analytical Data Architecture: Own the design and evolution of analytical data models, including facts, dimensions, aggregations, and materialized views, ensuring scalability, consistency, and reusability across business domains.
- Data Transformation & Engineering: Oversee the development of modular, maintainable, and high-performance SQL-based transformations that convert raw banking data into trusted, analytics-ready datasets.
- Pipeline Orchestration & Reliability: Ensure the reliability and scalability of automated data workflows, including scheduling, dependency management, monitoring, error handling, recovery, and delivery of data within agreed service levels.
- Performance & Scalability: Drive query optimization, database performance tuning, and efficient storage and processing strategies across relational and columnar databases, balancing performance, reliability, and infrastructure costs.
- Data Quality & Observability: Establish standards and automated mechanisms for data validation, freshness monitoring, anomaly detection, and pipeline observability. Ensure data issues are identified, prioritized, and resolved proactively.
- Semantic Layer & Metric Governance: Lead the standardization of business definitions, KPIs, and reusable analytical logic, ensuring consistent metric interpretation across dashboards, reports, and analytical products.
- Technical Standards & Best Practices: Define and enforce engineering standards for version control, code reviews, testing, documentation, deployment, and maintainability. Promote automation and continuous improvement across the development lifecycle.
- Planning & Delivery: Translate business and analytical requirements into technical roadmaps, prioritize work based on business impact and technical risk, manage dependencies, and ensure predictable delivery of engineering commitments.
- Documentation & Governance: Ensure appropriate documentation of data models, business logic, data lineage, ownership, and operational procedures, supporting transparency, auditability, and long-term maintainability.
نیازمندیها
- Demonstrated experience leading, mentoring, or technically guiding an engineering team.
- Expert-level SQL skills, including complex transformations, analytical functions, query optimization, and performance tuning.
- Strong Python skills for automation, data validation, transformation, and engineering utilities.
- Hands-on experience with relational databases such as PostgreSQL, including schema design, indexing, partitioning, and query optimization.
- Practical experience with columnar analytical databases such as ClickHouse, including data modeling, table engine selection, and performance optimization.
- Production experience with Apache Airflow, including DAG development, scheduling, dependency management, monitoring, and troubleshooting.
- Strong understanding of data warehouse architecture, ETL/ELT patterns, dimensional modeling, and analytical data lifecycle management.
- Experience designing and maintaining data models and analytical assets for BI platforms such as Metabase or Superset.
- Proficiency with Git and collaborative engineering practices, including code reviews, testing, and technical documentation.
- Experience in banking, fintech, or other financial services environments.
- Experience with data lake architecture, layered data models (Raw, Silver, Gold), partitioning strategies, and columnar file formats such as Parquet.
- Familiarity with NoSQL and search technologies such as MongoDB and Elasticsearch.
- Experience implementing data quality frameworks, observability, lineage, and data catalog solutions.
- Familiarity with CI/CD, automated testing, and deployment practices for data pipelines and transformations.
- Experience building scalable analytical foundations for advanced analytics, machine learning, and AI-driven products.