Job Opportunity Posted 7 days ago

Data Support Specialist

NexionPro Services

Job Description

Role Overview

We are looking for a proactive Data Support Engineer to join our data engineering team. The role involves supporting, monitoring, and troubleshooting data pipelines and ETL processes, ensuring data reliability and smooth operations across Snowflake-based data platforms.

Key Responsibilities

  • Monitor and support daily data pipeline operations, ensuring timely and accurate data delivery.
  • Perform L1/L2 troubleshooting for ETL job failures, data discrepancies, and pipeline errors.
  • Write and optimize SQL queries for data validation, reconciliation, and issue resolution.
  • Support and maintain Snowflake data warehouse objects, queries, and performance issues.
  • Assist in developing and maintaining ETL workflows using Python and PySpark.
  • Perform root-cause analysis for recurring data issues and support permanent fixes.
  • Collaborate with data engineers and business teams to resolve data quality and pipeline-related tickets.
  • Document issues, resolutions, and standard operating procedures for recurring support scenarios.
  • Support production releases, deployments, and post-deployment validation.

Mandatory Skills

  • Snowflake – Hands-on experience with querying, basic administration, and troubleshooting
  • ETL – Understanding of ETL/ELT concepts and pipeline workflows
  • SQL – Strong query writing, debugging, and optimization skills
  • Python – Working knowledge for scripting and automation
  • PySpark – Basic to intermediate hands-on experience

Good to Have

  • Experience with cloud platforms (Azure/AWS/GCP)
  • Exposure to job scheduling/monitoring tools
  • Ticketing tool experience (ServiceNow, JIRA, etc.)
  • Basic understanding of data warehousing concepts
  • Experience in a production support/L1-L2 support environment

Ideal Candidate

A candidate with 1–3 years of experience who is comfortable working in a support-driven environment, has hands-on exposure to Snowflake, SQL, Python, and PySpark, and can quickly troubleshoot and resolve data pipeline issues under time-sensitive conditions.

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