Senior Data & Analytics Engineer (Azar)

[About the Platform Department]

The Platform Department brings together Data Engineering, SRE/DevOps, and MLOps specialists who are responsible for the development and operation of the centralized platform and system infrastructure. We provide the common platform infrastructure and technologies for all company services, such as Azar and AI/ML, and actively collaborate with relevant departments to achieve business results in various areas. Furthermore, we focus on creating an efficient and productive engineering culture, preventing fragmentation among technical teams within the company.

[Data Analytics Engineering Team]

• The Data Analytics Engineering team is responsible for Data Governance, Analytics Engineering, and BI/DW Engineering.
• We go beyond simply managing data pipelines, by developing and providing robust data pipelines and data models that create valuable data and manage SLO/SLI for data quality.
• When managing and developing DataLake/Warehouse, we implement modern technological paradigms such as Data Governance and Analytics Engineering, and apply software development practices including version control, testing, deployment, monitoring, and observability to our data platform.
• Data as a Product: we view and develop data as a software product. We also act as a bridge between data producers and consumers, aiming to improve the data creation process for producers (Shift Right) and simplify its usage for consumers (Shift Left).
• We create and manage data documentation and metadata to enhance data literacy within the organization and improve the understanding and utilization of data models.
• We actively participate in the development of internal and external data products for monetization, statistics, and operational activities, based on aggregated data models.

[By joining the team, you will]

• Independently manage large-scale global data coming from various domain environments.
• Overcome the limitations of simply maintaining data pipelines and participate in the creation and design of systems to solve data-driven business problems.
• Gain experience in developing essential Data Applications for business.
• Work with massive data volumes (tens of TB/day and more) on a global scale, allowing you to tackle diverse technical challenges and experiment.
• Continuously explore best approaches and rationally implement new workflows or systems into production.
• Work with EKS, BigQuery, Databricks, Airflow, DBT, gaining experience with various cloud infrastructures and frameworks.

Responsibilities: Develop and operate Silver+/Mart products post-Ledger using Airflow + dbt, taking full responsibility for their consistency, reproducibility, and reliability. Create and manage metric and semantic layers (standardization of metric definitions, calculation formulas, versions, and impact of changes). Ensure operational activities for data quality and observability (dbt tests, custom checks, anomaly detection for key metrics, reliability scoring models). Standardize analytics service (collaborate with PA, PM, MKT, FP&A for monitoring dashboards, reports, KPIs). Contribute to the automation of experiments/analytics, Reverse ETL specifications, and data governance (schema, terminology, ownership). Compliance Engineering (Privacy, DLP): define blocking requirements at the ingestion stage, anonymization/pseudonymization/aggregation, implementation, egress events, periodic data deletion/anonymization, application of DLP policies (Data Loss Prevention) at the data level.

Requirements: Hands-on experience in data pipeline engineering - SQL + Python, Airflow/dbt operation. Understanding of data warehouse modeling and performance (Databricks / BigQuery, etc.). Experience applying software development methodologies such as testing, CI/CD, version control to data pipelines. Experience in transforming business domains into robust data products. Ability for independent design and responsibility, as well as cross-team communication and coordination skills.

Preferred Qualifications: Experience in independently building a semantic layer / MetricFlow, data quality frameworks. Experience handling large volumes of data traffic, real-time/near real-time data processing. Experience in data privacy/compliance engineering or building/operating DLP. Strengths in one or more of the following areas: Product Analytics / Growth/Marketing / FP&A / AI/ML.

Hiring Process: Employment Type: Full-time Selection Stages: Resume Screening > Coding Test > Recruiter Call > 1st Interview > 2nd Interview > Final Decision (*stages may be added/modified as needed). Application Documents: Resume in Russian or English (PDF) in free form with detailed work experience description.

Providing false information or having obstacles to employment in accordance with the law may lead to the cancellation of the job offer. Additional checks may be conducted as needed, beyond the announced selection stages. Benefits are provided to individuals who are classified as combat veterans in accordance with current legislation. Please inform us of your status when applying and provide supporting documents.