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.

We would be thrilled if you have:

• 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.
  

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.