Senior Data Engineer

Experience: 6+ years
Remote, full-time

Domain:

Marketing Technology

This project is designed for consulting companies that provide analytics and predictions to subscription-based business customers based on their website users' behavior and other finance and transactional data (conversions, registrations, payments, etc.). It integrates world-class data science to improve customer engagement by:
- analyzing Audience;
- optimizing Pricing strategy;
- making recommendations on Paywall strategy;
- making recommendations on content.
Based on the received analytics results, customers can make decisions about the development of their own products, decisions in real time in the context of a particular user due to the possibility of automated access to analytics.
Product works as well for a bigger variety of industries: telco, eCommerce, financial, healthcare, media & publishing, retail, sports etc.

What we expect:

successfully implemented and released data integration services or APIs using modern Python frameworks in the past 4 years;
successfully designed data models and schemas for analytics or data warehousing solutions;
strong analysis and problem solving skills;
strong knowledge of Python programming language and data engineering;
deep understanding of good programming practices, design patterns, and software architecture principles;
ability to work as part of a team by contributing to product backlog reviews and solution design and implementation;
be disciplined in implementing software in a timely manner while ensuring product quality isn't compromised;
formal training in software engineering, computer science, computer engineering, or data engineering;
have working knowledge with Apache Airflow or a similar technology for workflow orchestration;
have working knowledge with dbt (data build tool) for analytics transformation workflows;
experience with cloud data warehouses (Snowflake preferred) and distributed computing frameworks (Apache Spark);
experience integrating with event tracking platforms (e.g., Google Analytics 4, Segment, Amplitude) and third-party data sources;
understanding of data modeling concepts including dimensional modeling, slowly changing dimensions, and incremental loading patterns;
have working knowledge with containerization (Docker/Kubernetes) and CI/CD pipelines;
successfully implemented data APIs and integration services that seamlessly connect various data technologies;
English level: B2 (Upper-intermediate).

What you will do:

work in an agile team to design, develop, and implement data integration services that connect diverse data sources including event tracking platforms (GA4, Segment), databases, APIs, and third-party systems;
build and maintain robust data pipelines using Apache Airflow, dbt, and Spark to orchestrate complex workflows and transform raw data into analytics-ready datasets in Snowflake;
develop Python-based integration services and APIs that enable seamless data flow between various data technologies and downstream applications;
collaborate actively with data analysts, analytics engineers, and platform teams to understand requirements, troubleshoot data issues, and optimize pipeline performance;
participate in code reviews, sprint planning, and retrospectives to ensure high-quality, production-ready code by end of each sprint;
contribute to the continuous improvement of data platform infrastructure, development practices, and deployment processes in accordance with CI/CD best practices.