Senior Analytics Engineer (Data Usage)

We are currently actively building a Data Warehouse a key part of the product. We work with cutting edge technologies (GCP, AWS, Airflow, Kafka, K8s) and make infrastructure and architectural decisions based on data. We are building a large scale data infrastructure for analytics, machine learning, and realtime recommendations. Our tech stack Languages: Python, SQL Frameworks: Spark, Apache Beam Storage and analytics: BigQuery, GCS, S3, Trio, other GCP and AWS stack components Integration: Apache Kafka, Google Pub/Sub, Debezium ETL: Airflow 2 Infrastructure: Kubernetes, Terraform Development: GitHub, GitHub Actions, Jira Key Responsibilities Gather and clarify requirements from diverse stakeholders across the company. Design and evolve DWH Architecture (ODS and Data Mart layers) with a focus on scalability, performance, and data security standards. Build robust and efficient incremental pipelines; develop and optimize data marts in BigQuery (Dataform/SQL/DBT) and Airflow. Participate in testing, data validation, and release processes Design and implement data quality checks; investigate data quality issues and consistency discrepancies across various pipelines. Perform deep-dive analysis of source systems to build efficient data flows from source to consumption. Maintain architectural and technical documentation to ensure data transparency and compliance. Skills, Knowledge and Expertise 3+ years of experience as an Analytics Engineer / DWH Engineer / Data Analyst working with DWH Hands-on experience with data warehouses Strong understanding of DWH architecture and data layers (ODS, Data Marts) Understanding of incremental loads, historical data handling, and deduplication Strong SQL skills Experience designing and optimizing data marts Experience with BigQuery (partitioning, clustering, cost-aware querying) Experience with Airflow or similar orchestration tools Python for data processing and ETL tasks Ability to work with stakeholders and translate business needs into data requirements Must have to be familiar with: Languages: SQL (strong knowledge), Python (basic knowledge) Orchestration: Airflow or similar orchestration tool Version Control: Git Experience with Data Quality / Data Governance / SLAs Nice to be familiar with: Cloud & Storage: Google Cloud Platform (BigQuery, Cloud Storage, Dataform, DBT) Conditions Stable salary, official employment; Health insurance; Hybrid work mode and flexile schedule; Relocation package offered for candidates from other regions; Access to professional counseling services including psychological, financial, and legal support; Discount club membership; Diverse internal training programs; Partially or fully payed additional training courses; All necessary work equipment.