Senior Data Engineer / AI Data Platform Engineer (Spark / ETL / Cloud)

Location:
Santo Domingo, Dominican Republic (On-site Hybrid Remote flexibility)

Employment Type:
Full-Time

Industry:
AI / Software Engineering / Data Platforms

About The Role
We are looking for a
Senior Data Engineer / AI Data Platform Engineer
to design and build
high-performance, scalable data pipelines
that power
AI/ML systems and data-driven applications
.

This role goes beyond traditional ETL — it focuses on
distributed computing, large-scale data processing, and data infrastructure for AI workloads
using technologies like
Apache Spark, Python, and cloud-native architectures
.

You will play a key role in enabling
machine learning pipelines, real-time data processing, and high-volume data systems
.

Key Responsibilities

• Design and build scalable data pipelines for AI/ML workloads
• Develop distributed data processing systems using Apache Spark (batch & streaming)
• Optimize large-scale data transformations using Python and SQL
• Architect and maintain data platforms on AWS, Azure, or Google Cloud
• Implement parallel processing, partitioning strategies, and performance tuning
• Enable data ingestion pipelines for structured and unstructured data (logs, events, APIs)
• Collaborate with ML Engineers and Software Engineers to support AI models
• Ensure data reliability, observability, and system scalability

Technical Requirements (MUST HAVE)

• 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–1B+ records)

Nice to Have (Highly Valued in AI Market)

• 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

What We Offer

• Competitive salary aligned with AI/Engineering market (USD-based)
• Flexible work model (Hybrid Remote)
• Opportunity to work on AI-driven systems and scalable platforms
• High-impact engineering environment (not BI / not reporting-focused)

Application Requirements (MANDATORY)
To be considered, candidates MUST submit:

• Updated Resume (CV)
• Updated LinkedIn profile link
• A short written response including:

Why should you be considered for this role?
Please include:

• 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)
Provide a concise summary of your experience as a
Data Engineer / AI Data Engineer
.

Li SEO Keywords (AI / Engineering Focus)
(keep small when posting)
Senior Data Engineer | AI Data Engineer | Data Platform Engineer | Apache Spark | Distributed Systems | Big Data | Python | SQL | AWS | Azure | GCP | Machine Learning Data Pipelines | Data Infrastructure | Spark Streaming | Kafka | Databricks | Data Lakehouse | MLOps | Cloud Engineering | Scalable Systems