Overview

We are looking for a skilled Data Engineer to design, build, and maintain the infrastructure that powers our data management, processing, and analytics efforts. You will work closely with cross-functional teams to develop scalable data pipelines, storage solutions, and governance frameworks to ensure data accuracy, accessibility, and security across the organization.

Responsibilities:
  • Design, develop, and maintain efficient, scalable data pipelines for collecting, processing, and storing large volumes of data
  • Build and manage modern data storage systems, including data lakes and data warehouses
  • Collaborate with cross-functional teams to define and implement data governance and compliance practices
  • Partner with data analysts and data scientists to optimize data architecture for reporting, analytics, and machine learning workloads
  • Monitor, troubleshoot, and resolve issues within data pipelines and storage infrastructure
  • Ensure data quality, accuracy, and integrity through validation and control mechanisms
  • Conduct code reviews and mentor junior engineers as needed
  • Stay current with industry trends, tools, and best practices in data engineering
  • Communicate with business stakeholders to understand data requirements and deliver solutions that meet their needs
Required Qualifications:
  • Bachelor’s degree in Computer Science, Information Systems, or a related field
  • 3+ years of hands-on experience in data engineering or a related field
  • Strong expertise in SQL and data modeling
  • Solid understanding of data storage and processing technologies, including:
  • Relational and NoSQL databases
  • Linux-based environments and Bash scripting
  • Data warehouses (e.g., Snowflake, Redshift, BigQuery)
  • Big Data technologies: Hadoop, Spark
  • Data streaming tools: Kafka, Spark Streaming
  • Workflow orchestration: Apache Airflow, AWS Glue, or similar
  • Proficiency in at least one programming language: Python, Java, or Scala
  • Experience with cloud-based data platforms (AWS, Azure, or Google Cloud)
  • Knowledge of ETL processes and tools
  • Strong problem-solving, communication, and collaboration skills
  • High attention to detail and ability to thrive in a fast-paced environment
  • Experience with CI/CD pipelines for data workflows
  • Familiarity with data security and compliance standards (e.g., GDPR, HIPAA)
  • Knowledge of version control systems such as Git
  • Exposure to data catalog and lineage tools (e.g., Apache Atlas, Amundsen)
  • Problem-solving
  • Communication
  • Collaboration
  • Attention to detail
Technologies:
  • SQL
  • Relational and NoSQL databases
  • Linux
  • Bash
  • Snowflake
  • Redshift
  • BigQuery
  • Hadoop
  • Spark
  • Kafka
  • Spark Streaming
  • Apache Airflow
  • AWS Glue
  • Python
  • Java
  • Scala
  • AWS
  • Azure
  • Google Cloud
  • ETL
  • CI/CD
  • Git
  • Apache Atlas
  • Amundsen
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