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

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.

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)

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Develop your professional skills with access to mentoring, career consulting, and learning programs.
Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
Work alongside people who take initiative, speak openly, and challenge themselves to grow.
Improve your language skills through co-financed courses and internal speaking clubs.

Final benefits may vary depending on the location.