We are seeking a senior-level Data Engineer with Machine Learning Analyst capabilities to lead the architecture, development, and management of scalable data solutions. The ideal candidate will bring strong expertise in data architecture, big data pipeline development, and data quality enhancement, along with experience in processing large-scale datasets and supporting machine learning workflows.
Key Responsibilities:
Design, develop, and maintain end-to-end data pipelines for ingestion, transformation, and delivery across various business systems.
Ensure robust data quality, data lineage, data reconciliation, and governance practices.
Architect and manage data warehouse and big data solutions supporting structured and unstructured data.
Optimize and automate ETL/ELT processes for high-volume data environments (processing 5B+ records).
Configure and manage client onboarding and data file setups for seamless integration.
Collaborate with data scientists and analysts to support machine learning workflows with clean, well-structured datasets.
Implement streamlined DAAS (Data as a Service) workflows to reduce processing time and improve delivery accuracy.
Monitor and enhance data quality metrics, troubleshooting and resolving data discrepancies proactively.
Must-Have Skills:
10+ years of experience in data engineering, including data architecture and pipeline development.
Proven experience with Spark and Hadoop clusters for processing large-scale datasets.
Strong understanding of ETL frameworks, data quality processes, and automation best practices.
Experience in data ingestion, lineage, governance, and reconciliation.
Solid understanding of data warehouse design principles and data modeling.
Expertise in automated data processing, especially for DAAS platforms.
Desirable Skills:
Experience with Apache HBase, Apache NiFi, and other Big Data tools.
Knowledge of distributed computing principles and real-time data streaming.
Familiarity with machine learning pipelines and supporting data structures.
Exposure to data cataloging and metadata management tools.
Proficiency in Python, Scala, or Java for data engineering tasks.
Soft Skills:
Strong analytical and problem-solving mindset.
Excellent communication skills to collaborate across technical and business teams.
Ability to work independently, manage multiple priorities, and lead data initiatives.
Required Skills for Data Engg With Machine Learning Analyst Job