Senior Data Engineer

2 Minutes ago • All levels • Data Analysis

Job Summary

Job Description

Ness Digital Engineering is seeking a Senior Data Engineer with strong problem-solving and debugging skills, a keen eye for detail, and high motivation. The role requires independent and collaborative work, leadership, and effective communication with stakeholders. This hybrid position involves working 3-4 days from the office. Key responsibilities include leveraging AWS services, SQL expertise, Python programming, and scripting for automation. Familiarity with Salesforce and Tableau is also important.
Must have:
  • Excellent problem-solving and debugging skills
  • Keen eye for detail
  • High level of commitment and motivation
  • Capable of working independently and collaboratively, taking the lead
  • Effective communication with management and stakeholders about issues, incidents, and updates
Good to have:
  • Data analysis and Salesforce knowledge
  • Knowledge of machine learning tooling/frameworks (scikit-learn or TensorFlow)
  • AI/LLM knowledge (foundation models, RAG, embeddings, vectors, prompt engineering, MCP)

Job Details

We are looking for a Senior Data Engineer with added emphasis on data analysis and Salesforce knowledge, which would be a nice-to-have. We are looking for someone who has excellent problem-solving and debugging skills, a keen eye for detail, and a high level of commitment and motivation. The ideal candidate should be capable of working independently and collaboratively, taking the lead. Effective communication with management and stakeholders about issues, incidents, and updates in a timely manner is also paramount. Further, this would require the Engineer to work in hybrid mode, 3-4 days from office.

Key requirements for the position:

  • A robust background in AWS services such as Lambda, Glue, S3, EMR, SNS, SQS, CloudWatch, Redshift, and Bedrock.
  • Strong expertise in SQL and relational databases like Oracle, MySQL, and PostgreSQL.
  • Familiarity with the Salesforce platform, including data models and objects.
  • Proficiency in Python programming for data engineering tasks.
  • Skilled in scripting for task and process automation.
  • Experience with Tableau dashboards and troubleshooting.

Some of the additional good to have skills are knowledge of machine learning tooling/frameworks (such as scikit-learn or TensorFlow) and AI/LLM knowledge, particularly with foundation models, RAG, embeddings, vectors, prompt engineering, and MCP.

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