Data Engineer

• Обʼєм даних - десятки рекламних мереж і платформ, сотні таблиць з сирими даними та тисячі аналітичних процесів щодня.
• Вартість запитів - при обсягах у сотні TB на день, невдалий запит без партиціонування — це різниця між витраченими $0.05 або $100+.
• Швидкість vs точність - маркетинг хоче коректний звіт "зараз", дані оновлюються з лагом 1-24 год.
• Приватність — PII-поля потребують окремої обробки на кожному етапі pipeline.
• На що ти будеш впливати: Бюджетні рішення - неточний ROAS = гроші йдуть у неефективний канал.
• Швидкість масштабування кампаній - затримка звіту на годину = втрачене вікно для оптимізації.
• Довіра команди до цифр - одна помітна помилка підриває довіру до всіх наступних звітів.
• Compliance-ризики - неправильна обробка PII = юридичні наслідки.

• Design, build, and evolve Data Warehouses, Data Marts, and enterprise data models to support analytics and business decision-making
• Design, implement, and optimize scalable ELT pipelines and data processing solutions, ensuring reliability, performance, and maintainability
• Drive technical decisions related to data architecture, platform evolution, data modeling approaches, and integration patterns
• Establish and maintain data governance practices, metadata standards, data lineage, security controls, and access management policies
• Ensure data quality, observability, monitoring, and operational excellence across the data platform
• Evaluate and introduce technologies, tools, and best practices that improve platform scalability, efficiency, and maintainability
• Support and enhance self-service analytics capabilities through Apache Superset and other BI solutions
• Collaborate closely with BI Engineers, Business Analysts, and business stakeholders to align data platform capabilities with organizational goals
• Mentor Data Engineers, conduct technical reviews, and promote engineering best practices across the team
• Apply AI-assisted engineering practices to improve development efficiency, delivery quality, and platform sustainability

Senior Data Engineer

• Build and manage high-velocity ETL/ELT pipelines and real-time data processing services using Golang.
• Ensure data quality and accessibility for our AI models.
• Implement data systems that handle multiregional data residency and synchronization across different geographic locations.
• Build and maintain event-driven systems and data streams using Kafka and Kafka Connect.
• Optimize your workflow using Claude and Cursor.
• Maintain the highest standards of data protection (GDPR, UAE PDPL).

• Design and develop scalable, cloud-native data architectures in BigQuery, ensuring optimal performance, maintainability, and cost efficiency
• Build, automate, and maintain ETL/ELT pipelines using modern data stack tools and custom solutions
• Create and optimize business-critical data marts, aggregations, and analytical datasets for reporting, dashboarding, and product insights
• Implement data quality frameworks, monitoring systems, logging, and alerting mechanisms to ensure data reliability
• Continuously improve performance, scalability, and cost efficiency of production data infrastructure
• Partner closely with Data Science teams to build reliable data services, infrastructure, and pipelines supporting machine learning and advanced analytics initiatives
• Collaborate with product, analytics, and engineering teams to define and deliver scalable data solutions across the organization

• Design and implement scalable data platforms and pipelines across cloud environments
• Develop reliable batch, streaming, and near-real-time pipelines using technologies such as Spark and Delta Lake
• Build ingestion, transformation, and curation workflows for both structured and unstructured data
• Implement modern data architectures including lakehouse patterns and medallion layering
• Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems
• Enable data pipelines for GenAI use cases
• Design scalable logical and physical data models
• Orchestrate workflows using tools such as Airflow, dbt, Lakeflow, or equivalents
• Apply modern architecture patterns including event-driven and streaming architectures
• Establish strong data observability, including monitoring of data freshness, pipeline reliability, and SLA adherence
• Enable data serving layers to support downstream systems

• Assist the Data Engineering team with migration from BigQuery to Snowflake
• Translate and optimize SQL queries across platforms
• Perform end-to-end validation to ensure data correctness and pipeline reliability
• Write and maintain incremental dbt models
• Collaborate closely with data engineers, analysts, and stakeholders to understand data needs and deliver solutions
• Participate in code reviews, share knowledge, and contribute to team best practices
• Document work clearly to support maintainability and knowledge sharing

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