indeed
Sr Data Ops Developer
Epicor Software
IND
Full-time
Onsite
Discovered 1 weeks ago
Cloud-based data pipelinesData ingestion and integrationData quality, integrity, and timelinessData lakes, data warehouses, and data martsAzure SparkAzure SQL Pools and data warehouses
Free
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Cloud-based data pipelinesData ingestion and integrationData quality, integrity, and timeliness
Key Skills for This Role
Cloud-based data pipelinesData ingestion and integrationData quality, integrity, and timelinessData lakes, data warehouses, and data martsAzure SparkAzure SQL Pools and data warehouses
Full Job Posting
What You Will Be Doing
- Perform duties according to company policies, procedures, internal controls, and SOX standards.
- Design, implement, and maintain cloud-based data pipelines for large volumes of data.
- Collaborate with data engineers and stakeholders on scalable data platform architectures.
- Evaluate cloud infrastructure and services for scalability, performance, extensibility, cost, and security.
- Implement ingestion pipelines across disparate data sources while maintaining data quality, integrity, and timeliness.
- Design and optimize data lakes, data warehouses, and data marts.
- Implement processing and transformation workflows using Azure Spark, Azure SQL Pools, data warehouses, and other cloud-native services.
What You Will Likely Bring
- At least 5 years of applicable experience and demonstrated success or knowledge.
- At least 2 years of specialized or industry experience.
- Bachelor’s degree or equivalent experience.
- Strong Excel skills and financial systems experience.
- Ability to synthesize information and tailor it to the required audience.
- Strong analytical, strategic thinking, communication, and problem-solving skills.
- Interest in process implementation and ongoing systems integration.
- Ability to work independently and as part of a team.
Differentiating Experience
- Azure development beyond data pipelines, including infrastructure as code with Bicep, Terraform, or ARM.
- DevOps practices including CI/CD, Git-based source control, and automated deployment of data assets.
- Use of AI coding assistants within disciplined, version-controlled workflows.
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