Cloud Data Engineer

PMTS LLC
Nashville, United States of America
4 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Intermediate

Job location

Nashville, United States of America

Tech stack

Java
Agile Methodologies
Amazon Web Services (AWS)
Data analysis
Apache HTTP Server
Confluence
Azure
Big Data
Cloud Database
Information Systems
Computer Programming
Information Engineering
ETL
Data Security
Data Warehousing
Github
Identity and Access Management
Python
Cloud Services
SQL Databases
Google Cloud Platform
Azure
Snowflake
Spark
GIT
PySpark
Information Technology
Amazon Web Services (AWS)
Data Management
Data Pipelines
Databricks

Job description

We are seeking a highly skilled Cloud Data Engineer with strong experience in Databricks, Snowflake, PySpark, and cloud-native data engineering technologies. The ideal candidate will work closely with financial services clients to design, build, and optimize scalable data platforms, ETL pipelines, and modern data lakehouse solutions., * Design, develop, and maintain scalable data pipelines using Databricks, Snowflake, PySpark, Python, and SQL.

  • Build and manage ETL workflows for ingesting, transforming, and processing large-scale datasets from multiple data sources.
  • Work with cloud-native data services such as AWS Glue, Azure Data Factory, or similar technologies.
  • Support data analysis, discovery, and reporting initiatives across financial services and risk/compliance domains.
  • Develop and optimize data warehouse and lakehouse architectures.
  • Evaluate and implement modern query engines, catalogs, and storage formats including Apache Iceberg and Dremio.
  • Collaborate with architects, stakeholders, and engineering teams in an Agile/Sprint environment.
  • Perform peer reviews, testing, validation, and deployment activities using Git/GitHub.
  • Document technical findings, implementation approaches, and outcomes in Confluence or related tools.
  • Ensure data security, governance, and access management using role-based policies.

Requirements

  • 5+ years of experience in Data Engineering.
  • Strong hands-on experience with Databricks and Snowflake.
  • 3+ years of experience with PySpark/Spark.
  • Strong programming skills in Python or Java.
  • Advanced SQL development and optimization skills.
  • Experience building and maintaining enterprise ETL/Data pipelines.
  • Strong understanding of Data Warehousing and Data Modeling concepts.
  • Experience with AWS, Azure, or Google Cloud Platform cloud platforms.
  • Experience with GitHub/Git version control.
  • Strong analytical, troubleshooting, and communication skills.

Preferred Skills:

  • Experience with Apache Iceberg, Dremio, or Lakehouse platforms.
  • Experience in financial services, investment management, risk, or compliance data.
  • Exposure to streaming/data movement technologies.
  • Experience working in Agile/Scrum delivery models., Bachelor s degree in Computer Science, Information Systems, Engineering, or related field preferred.

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