Senior Data Engineer

Join our Data Community and build scalable data solutions using Databricks, AWS and Python, from robust ETL pipelines to real-time data processing.

You might be our missing piece if you have:

• 5+ years of experience in data engineering, database development, and cloud-based data solutions (especially on AWS).
• Strong proficiency in SQL (T-SQL, PL/SQL) and experience with database technologies (e.g., Oracle, SQL Server, Snowflake/Redshift, Databricks).
• 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.

We would be thrilled if you have:

• Strong understanding of data lake architectures and advanced cloud-based data solutions (with depth in AWS data services).
• Familiarity with high-performance query optimization techniques for large-scale datasets (e.g., query tuning, indexing strategies).
• Exposure to data governance and metadata management systems (e.g., data catalog, lineage tools).
• 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 (e.g., Apache Spark or Flink) and experience with CI/CD pipelines or Infrastructure-as-Code for data engineering projects.
• A sense of belonging while reading about our culture.

We will be working together on:

• 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.