Data Engineer

Job Requirements

Primary Responsibilities

• Minimum 2 to 3 years of hands-on experience in data engineering.
• Proven expertise in SQL, Spark, and the Hadoop ecosystem.
• Experience handling multiple terabytes of data from ingestion to consumption.
• Collaborate with business stakeholders to identify and document high-impact business problems and potential solutions.
• Strong understanding of Data Lake/Lakehouse architecture and exposure to Hadoop (Cloudera, Hortonworks) and/or AWS.
• Manage the full data lifecycle: ingestion, transformation, and consumption.
• Proficient in Spark, Spark Streaming, Hive, and SQL.
• Experience with big data infrastructure including MapReduce, Hive, HDFS, YARN, HBase, Oozie, etc.
• Deep knowledge of relational databases and data modeling.
• Technical debugging skills and experience with Git for version control.
• Create and maintain technical design documentation for data pipelines and projects.
  

• Ability to work independently and manage personal development efforts.
• Strong oral and written communication skills.
• Learn and apply internal analytics technologies.
• Identify key performance indicators and develop strategies to achieve them.
• Use educational background in data engineering to perform data mining and analysis.
• Collaborate with BI analysts/engineers to build prototypes.
• Participate in the delivery and presentation of data solutions.
  

• Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or a related field.
  

Experience

• 2 to 5 years of relevant experience in data engineering
  

• Strong coding and debugging skills.
• Proficiency in big data tools and cloud platforms.
• Ability to work with large datasets and complex data architectures.
• Effective communication and stakeholder engagement.
• Self-driven with a continuous learning mindset.
• Strong documentation and organizational skills.
  

• Timely and high-quality delivery of data engineering projects.
• Excellent code quality and maintainability.
• Identification and resolution of data issues.
• Effective technical solutioning and error-free execution.