Data Engineer (Hybrid or Onsite)
Core
Design and implement ETL pipelines and data architectures to aggregate, transform, and load data from diverse sources into warehouses and lakes for business intelligence and analytics.
Role type
Data Engineer (ETL & Data Pipeline)
Builds
Robust ETL pipelines, data warehouses, data lakes, and SQL databases
Domain
Fintech / Payments / Banking
Deliverable
production ML models | product features | dashboards & analysis
Required skills
SQL, Azure technologies, ETL tools (SSIS, Azure Data Factory), Relational databases (SQL Server, Oracle, MySQL), NoSQL databases (Mongo, Impala, CouchDB), Data Visualization tools (PowerBI, Tableau), Python
Preferred skills
AI/ML frameworks, Cloud services (AWS), Cloud Data Warehouses (Azure Data Warehouse), Analytics Platforms (Databricks, Microsoft Fabric), Streaming Platforms (Apache Kafka, Azure EventHub), Agile methodologies, Git, AI and Machine Learning Modeling tools
Responsibilities
Design and implement solutions for optimal extraction, transformation, and loading of data; Integrate AI-driven analytics tools into data pipelines; Leverage machine learning algorithms to optimize ETL processes; Design, develop and maintain new and existing data and ETL pipelines; Create optimal data pipeline architecture and systems; Identify, design, and implement internal process improvements by automating manual processes
Seniority
Mid-level to Senior, hands-on IC
