Data Engineer

Responsibilities

• Data Pipeline Engineering & Integration
• Build and operationalize data pipelines across heterogeneous environments, aligning to governance principles and service expectations (SLAs).
• Build and maintain ingestion, transformation, and publication of pipelines (data engineering practice) to deliver analytics-ready data.
• Consolidate data from multiple sources into a centralized integration point (e.g., a single SQL Server instance) and manage field mappings and transformations to support consistent downstream consumption.
• Data Platform & Storage Design and implementation
• Design and implement data pipelines using Azure data technologies (e.g., Azure Data Factory, Azure Databricks, Azure Event Hubs, SSIS) to ingest, process, and deliver data from sources such as APIs and other systems.
• Build and maintain data warehousing capabilities (e.g., Azure Synapse Analytics) to support analytics and reporting workloads.
• Data Quality, Reliability & Operations
• Identify, troubleshoot, and resolve data issues including data quality, integrity, latency, and security concerns; apply monitoring and operational best practices to keep pipelines reliable and performant.
• Contribute to data quality and governance practices, including profiling datasets, defining quality rules, and establishing monitoring/remediation approaches.
• Collaboration & Delivery (Agile Pod Model)
• Work cross-functionally with engineers, analysts, and stakeholders to understand requirements and deliver data solutions that support sprint-based delivery.
• Support pod-level delivery by producing reusable data assets and integration components that can be leveraged across multiple initiatives.

Requirements

• Proficiency in SQL and Python.
• Data pipeline tooling and cloud data services experience (Azure Data Factory, Azure Databricks, Azure Event Hubs, SSIS).
• Data warehousing experience (Azure Synapse Analytics) and strong fundamentals in data modeling, warehousing, and governance.
• Scripting/automation skills (PowerShell and related tooling) for platform operations and troubleshooting.
• Preferred experience includes familiarity with additional programming languages such as Java, Scala, or Go; experience integrating data from multiple enterprise source systems into a central SQL-based integration layer; and familiarity with DataOps concepts and operating in cross-functional teams that include data engineering personas.

Qualifications

• Minimum Education: High School or GED.
• Preferred Education: Bachelor's Degree in Computer Science, Engineering, or related field.
• Minimum Years Of Experience: 3 years of experience in data engineering and/or data platform engineering (pipelines, integration, and operational support).