About the Position

Senior Data Engineer

You might be our missing piece if you have:

5+ years of experience in Data Engineering, including hands-on experience with Databricks, building cloud-based data solutions (Azure/AWS).
Strong proficiency in SQL (T-SQL, PL/SQL) and experience with database technologies (e.g., Oracle, SQL Server, Snowflake/Redshift).
Hands-on experience with ETL/ELT tools and frameworks, including modern cloud integration services (e.g., AWS Glue, Apache Airflow or Azure Data Factory).
Experience with data modeling, data integration, and data warehousing concepts.
Strong programming skills in Python (e.g., Pandas, automation scripting) for ETL pipeline development.
Knowledge of data governance and data quality frameworks, as well as security best practices for data (e.g., GDPR).
Experience working in Agile development environments, with collaborative tools and iterative processes.

Designing, developing, and maintaining data pipelines – including both batch ETL processes and real-time streaming solutions – to support our product teams. This includes implementing and managing Snowplow-based event tracking pipelines to collect, validate, and process user behavioral data in real time for analytics and product insights.
Collaborating with cross-functional teams (product managers, analysts, data scientists, etc.) to understand data needs and deliver insightful, scalable data solutions.
Replicating and generalizing successful data pipeline patterns to accelerate new pipeline development and ensure consistency and reliability across projects.
Developing reusable data processing utilities and tooling (leveraging common data-centric libraries and frameworks in Python) to streamline ETL/ELT workflows.
Optimizing database performance and ensuring high reliability of our data stores by performing query optimization, indexing, and tuning of SQL queries.

5+ years of experience in Data Engineering, including hands-on experience with Databricks, building cloud-based data solutions (Azure/AWS).
Strong proficiency in SQL (T-SQL, PL/SQL) and experience with database technologies (e.g., Oracle, SQL Server, Snowflake/Redshift).
Hands-on experience with ETL/ELT tools and frameworks, including modern cloud integration services (e.g., AWS Glue, Apache Airflow or Azure Data Factory).
Experience with data modeling, data integration, and data warehousing concepts.
Strong programming skills in Python (e.g., Pandas, automation scripting) for ETL pipeline development.
Knowledge of data governance and data quality frameworks, as well as security best practices for data (e.g., GDPR).
Experience working in Agile development environments, with collaborative tools and iterative processes.

Strong understanding of data lake architectures and advanced cloud-based data solutions.
Familiarity with high-performance query optimization techniques for large-scale datasets (e.g., query tuning, indexing strategies).
Experience with real-time streaming data frameworks and messaging systems (e.g., Apache Kafka, Amazon Kinesis) for event processing and ingestion.
Experience with Snowplow or similar event data tracking pipelines, including their implementation, maintenance, and optimization for behavioral data collection and analytics.
Knowledge of big data processing frameworks such as Apache Spark/PySpark, and experience with CI/CD pipelines or Infrastructure-as-Code for data engineering projects.

A sense of belonging while reading about our culture.