Responsibilities :

- Design, build, and manage scalable data pipelines, ensuring user event data is reliably ingested into the data warehouse.

- Develop and maintain canonical datasets to track key product metrics such as user growth, engagement, retention, and revenue.

- Collaborate with Infrastructure, Data Science, Product, Marketing, Finance, and Research teams to understand data needs and deliver effective solutions.

- Implement robust, fault-tolerant systems for data ingestion, transformation, and processing.

- Participate actively in data architecture and engineering decisions, contributing best practices and long-term scalability thinking.

- Ensure data security, integrity, and compliance in line with company policies and industry standards.

- Monitor pipeline health, troubleshoot failures, and continuously improve reliability and performance.

Requirements :

- 3-5 years of professional experience working as a data engineer or in a similar role.

- Proficiency in at least one data engineering programming language, such as Python, Scala, or Java.

- Experience with distributed data processing frameworks and technologies such as Hadoop, Flink, and distributed storage systems (e. g., HDFS).

- Strong expertise with ETL orchestration tools, such as Apache Airflow.

- Solid understanding of Apache Spark, with the ability to write, debug, and optimize Spark jobs.

- Experience designing and maintaining data pipelines for analytics, reporting, or ML use cases.

- Strong problem-solving skills and the ability to work across teams with varied data requirements.

Desired Skills :

- Hands-on experience working with Databricks in production environments.

- Familiarity with the GCP data stack, including Pub/Sub, Dataflow, BigQuery, and Google Cloud Storage (GCS).

- Exposure to data quality frameworks, data validation, or schema management tools.

- Understanding of analytics use cases, experimentation, or ML data workflows.