Data Engineer, Specialist
Role details
Job location
Tech stack
Job description
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Design, build, and maintain scalable ETL/ELT pipelines for critical IT data sources
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Develop curated, high-quality datasets to support reporting, dashboards, and analytics products
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Write, optimize, and maintain SQL and Python code for transformation, automation, and performance
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Deliver cloud-based data solutions using AWS services such as Glue, S3, and Athena
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Partner with analysts, product managers, and stakeholders to translate data needs into practical solutions
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Implement data quality checks, validation rules, monitoring, and documentation to ensure trusted data
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Troubleshoot pipeline issues, investigate anomalies, and resolve data defects end-to-end
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Apply engineering best practices including version control (Git), code reviews, testing, and CI/CD deployment
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Contribute to standards for data lineage, governance, documentation, and production support
Requirements
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Bachelor's degree or equivalent combination of education and relevant experience
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3-5+ years of experience in data engineering, analytics engineering, or data platform development
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Strong proficiency in SQL and Python for data transformation and automation
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Hands-on experience building and supporting ETL/ELT pipelines
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Experience with AWS or similar cloud data platforms
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Solid understanding of data modeling, curated datasets, and BI/reporting use cases
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Demonstrated problem-solving skills and commitment to data quality and reliability
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Ability to communicate effectively with both technical and non-technical stakeholders
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Experience working in collaborative, agile team environments
Preferred:
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Experience with AWS Glue, PySpark, Athena, Dremio, Parquet
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Exposure to BI and visualization tools such as Tableau, Power BI, or Streamlit
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Familiarity with Jira, GitHub, ServiceNow, or technology operations data
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Interest in building reusable data products and enabling self-service analytics
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Exposure to AI/ML concepts, LLMs, or agent-based tools to enhance data engineering workflows and automation