Senior Data Engineer / AI Data Platform Engineer (Spark / ETL / Cloud)
HybridSanto Domingo, Nacional, Dominican Republic
Santo Domingo, Nacional, Dominican RepublicHybridFull TimeSenior LevelSmall
Full TimeSenior LevelSmall
Job Summary
Design and build high-performance, scalable data pipelines for AI/ML workloads using Apache Spark, Python, and SQL. Develop distributed batch and streaming systems while optimizing large-scale data transformations on AWS, Azure, or Google Cloud. Enable ingestion for structured and unstructured data, collaborate with ML Engineers, and ensure data reliability and observability. This role focuses on distributed computing and data infrastructure for AI workloads rather than traditional ETL or BI reporting.
Required Qualifications
- Strong experience with Apache Spark (core + performance optimization)
- Advanced SQL (analytical + optimization level)
- Strong programming in Python (data + performance oriented)
- Proven experience building ETL / ELT pipelines at scale
- Experience with cloud-native architectures (AWS, Azure, or GCP)
- Deep understanding of distributed systems and data processing at scale
- Experience handling large datasets (10M–B+ records)
- Updated Resume (CV)
- Updated LinkedIn profile link
- A short written response including: Why should you be considered for this role?
- Your experience with Apache Spark and distributed data systems
- A description of the most complex data pipeline or system you have built
- Your experience working with large-scale data or AI-related systems
- Why you are a strong fit for this position
- Professional Summary (3–5 lines)
Desired Qualifications
- Experience supporting ML pipelines (feature engineering, data prep)
- Familiarity with Spark Streaming / Kafka / real-time pipelines
- Experience with Databricks / Snowflake / BigQuery
- Knowledge of data lakehouse architectures
- Experience with containerization (Docker, Kubernetes)
- Exposure to MLOps workflows
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