Key skills
SQLPythonAWSGCPAzureApache AirflowPrefectApache SparkKafkaFlinkSnowflakeBigQueryRedshiftData ModelingSchema DesignData ArchitectureAIMLMLOpsETLELTData EngineeringData WarehousingAnalyticsSparkAirflowdbt
About this role
tapouts is dedicated to nurturing the emotional and psychological well-being of children. They are looking for a Senior Data Engineer to design, build, and maintain scalable data infrastructure that supports analytics and business operations.
Responsibilities:
- Design, build, and maintain robust, scalable data pipelines (batch and real-time/streaming)
- Design and develop dashboards that surface key business metrics and enable strategic, data-informed decision-making
- Develop and optimize complex SQL queries, stored procedures, and data models
- Write clean, production-grade Python code for data ingestion, transformation, and automation
- Build and manage cloud-native data infrastructure on AWS, GCP, or Azure
- Implement and maintain data lakehouse architectures (e.g., Delta Lake, Apache Iceberg)
- Support ML workflows including feature engineering, model training pipelines, and MLOps integration
- Ensure data quality, governance, and lineage tracking across all data assets
- Collaborate with data scientists and analysts to deliver trusted, well-documented datasets
- Monitor pipeline performance, troubleshoot issues, and optimize for cost and efficiency
- Contribute to the development of internal data platform tools and frameworks
- Apply data governance best practices and ensure compliance with data privacy regulations (GDPR, LGPD)
Requirements:
- 5+ years of experience in data engineering or a related field
- Advanced English
- Strong proficiency in SQL — writing complex queries, optimizing performance, and data modeling
- Strong proficiency in Python — building ETL/ELT pipelines, scripting, and automation
- Experience with cloud platforms: AWS, GCP, or Azure
- Hands-on experience with data orchestration tools (Apache Airflow, Prefect, or similar)
- Experience with big data frameworks (Apache Spark, Kafka, Flink, or similar)
- Familiarity with data warehousing solutions (Snowflake, BigQuery, Redshift, or similar)
- Strong understanding of data modeling, schema design, and data architecture principles
- Experience with dbt (data build tool) and the modern data stack
- Familiarity with streaming and event-driven architectures
- Knowledge of MLOps and AI pipeline support
- Experience with data mesh or data platform engineering
- Familiarity with data governance frameworks and tools (data lineage, data cataloging)