Senior Data Engineer - Mobile Apps
New
J
JobgetherData Engineering
Based in United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Advanced English proficiency, both written and spoken
- Required Skills
- PythonSQLApache AirflowGCPSparkBigQuerydbtDatabricks
Requirements
- Proven experience in data engineering, with strong hands-on expertise in Python, SQL, Spark, and Databricks.
- Experience working with Databricks technologies such as Unity Catalog and Delta.
- Practical experience with Apache Airflow for workflow orchestration and scheduled production data pipelines.
- Experience with Google Cloud Platform, particularly Google Cloud Storage (GCS) and BigQuery.
- Strong analytical and problem-solving abilities, with a strategic and structured approach to resolving complex technical challenges.
- Experience taking ownership of production data environments, including scheduled jobs, data quality checks, alerting, monitoring, and incident resolution.
- Strong collaboration and communication skills, with the ability to work effectively with cross-functional stakeholders at different levels.
- Advanced English proficiency, both written and spoken, for clear communication in an international working environment.
- Solid understanding of agile methodologies and the ability to manage multiple projects without compromising quality.
- Curiosity about the business context behind data, with an interest in areas such as marketing spend, subscriptions, payments, and attribution.
- Experience with dbt and APIs from advertising or payment platforms such as Google Ads, Stripe, RevenueCat, or similar is a plus.
Responsibilities
- Build, maintain, and operate reliable data pipelines that support daily reporting, attribution, LTV analysis, and other critical business use cases.
- Ingest and transform data from advertising platforms, payment systems, and product backends into Databricks-based data environments.
- Orchestrate scheduled data workflows using Apache Airflow and ensure critical jobs complete accurately and on time.
- Maintain accurate and reliable silver and gold data tables while implementing appropriate data quality checks and monitoring.
- Proactively identify, troubleshoot, and resolve pipeline and production issues, including recovering failed daily runs within required timelines.
- Develop and improve processes, tools, and data solutions that enhance operational efficiency and support evolving business requirements.
- Collaborate with Analytics, Marketing, Data Science, Product, Engineering, and other stakeholders to understand requirements and evaluate technical approaches.
- Balance multiple projects and priorities while maintaining high standards of data quality, reliability, and delivery.
- Contribute to the continuous improvement of data engineering practices, processes, and methodologies within an agile environment.
- Apply a business-oriented perspective to data engineering, understanding how marketing investment, subscriptions, payments, and attribution influence organizational performance.
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