Senior Analytics Engineer(Data Ops)

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, Trino, 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.