Azure Data Engineer MAHIN-JOB-32546

Keylent, Inc.

$90K — $130K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years of experience in data engineering or related fields
  • Proficiency in Microsoft Azure services and data tools
  • Strong knowledge of SQL Server, Oracle, and ETL processes
  • Experience with monitoring tools like Striim
  • Familiarity with Linux operating systems
  • Knowledge of automation tools such as Ansible or Docker

Responsibilities

  • Monitor the performance and health of data pipelines and systems
  • Investigate and troubleshoot data pipeline issues and anomalies
  • Collaborate with teams to deploy and validate new data pipelines
  • Ensure data quality by regular validation and maintenance
  • Implement automation for data pipeline deployments
  • Engage with cross-functional teams to address data needs
  • Respond to alerts to ensure data reliability and availability

Benefits

  • Day 1 onsite presence in Alpharetta, GA
  • Opportunity for collaboration with cross-functional teams
  • Focus on continuous improvement through deployment automation
  • Exposure to advanced monitoring tools and technologies
  • Engagement with data governance policies and quality maintenance
Full Job Description
Azure Data Engineer MAHIN-JOB-32546
Position :- Azure Data Engineer
Location :- AlpharettaGA (Day 1 Onsite)
"Monitoring Data Pipelines and Systems:
  • Continuously monitor the health and performance of data pipelines (Striim), databases, and Linux servers.
  • Utilize monitoring tools and dashboards to track data flow, system resources, and data quality.
  • Respond promptly to alerts and anomalies to ensure data availability and reliability.
Issue Resolution:
  • Investigate and troubleshoot issues and anomalies detected during monitoring.
  • Identify the root causes of problems related to data pipelines, Striim, ETL processes, SQL Server, Oracle, or Linux systems.
  • Develop and implement solutions to resolve data-related issues promptly.
Deployment of Data Pipelines:
  • Collaborate with development teams to deploy new data pipelines, updates, and changes.
  • Ensure proper testing and validation of ETL processes and data pipelines before deployment.
  • Monitor the deployment process to identify and address any issues in real-time.
Data Quality Maintenance:
  • Regularly check and maintain data quality by validating data against predefined standards and data governance policies.
  • Fix data quality issues and errors in data pipelines to maintain accurate data.
Deployment Automation:
  • Implement automation for deploying data pipelines and updates to improve efficiency and reduce deployment errors.
  • Use tools like Ansible or Docker for containerization and orchestration.
Collaboration:
  • Collaborate with cross-functional teams, including data scientists, analysts, and software developers, to understand their data needs and provide.
  • Engage in regular communication to ensure alignment on data-related activities and issue resolution. "

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