بلوبانک | blu Bank

بانکداری ۵۰۱ تا ۱۰۰۰ نفر blubank.com

فرصت‌های شغلی ۱۳

استخدام Data Analytics Engineering Team Lead

دسته‌بندی شغلی

وب،‌ برنامه‌نویسی و نرم‌افزار
موقعیت مکانی

تهران ، تهران
نوع همکاری

تمام وقت
حداقل سابقه کار

سه تا شش سال
حقوق

توافقی

شرح موقعیت شغلی

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 Responsibilities:

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.

Required Qualifications:

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.

Preferred Qualifications:

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.

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معرفی شرکت

ما معتقدیم که در دنیای امروز، باید جایگزینی برای ساختار پرداختی و بانکی سنتی فعلی وجود داشته باشد و این دقیقا دلیل وجود ماست تا رویکردی مدرن و به‌روز برای کشورمان ایجاد کنیم. نقطه‌ی آغازین این رسالت مهم با همکاران ما در بلو شروع می‌شود. ما در بلو به‌دنبال آفرینش گونه‌ی جدیدی از راهکارهای پرداخت و مدیریت مالی هستیم که هم‌راستا با نیازهای شما در دنیای مدرن امروز، سریع و به‌دور از نیازهای بروکراتیک فعلی باشد.
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ما در«بلو» با فرهنگی رشد می‌کنیم که در آن دو روز کاری هیچ شباهتی با هم ندارند؛ ارزش‎‌آفرینی ما در حل چالش‌ها و انجام کارهاست، نه فقط دنبال کردن پروسه‌های کاری تعریف‌شده و یکنواخت! فرهنگ کاری ما همکاران را تشویق می‌کند تا ایده‌های‌شان را با ما در میان بگذارند؛ چه ایده‌های خلاقانه بزرگ‌شان و چه ایده‌هایی که لزوما فقط بزرگ به‌نظر می‌رسند! اینجا سفری را تجربه می‌کنید که با هیجان، خلاقیت و به‌سرعت اتفاق می‌افتد. پس اگر دوست دارید در این تجربه با ما همراه باشید، رزومه‌ی خود را برای ما ارسال کنید.