Strong ADF Developer Required

Job Description Mid-Level Azure Data Factory Developer – Enterprise Integrations Position Overview We are seeking a Mid-Level Azure Data Factory (ADF) Developer to develop, deploy, monitor, troubleshoot, and support enterprise data integrations across business applications and the Enterprise Data Warehouse (EDW). The role is primarily focused on integration delivery and production support. The successful candidate will build reliable data pipelines, implement approved source-to-target mappings, perform data validation and transformation, monitor integrations, handle exceptions and retries, and ensure data movement is accurate, traceable, and supportable. This is not a data-copy-only role. The candidate must understand enterprise integration reliability, reconciliation, monitoring, exception handling, and the downstream business impact of data failures. Key Responsibilities Azure Data Factory Development Design, develop, test, deploy, and maintain Azure Data Factory pipelines. Build reusable pipelines using ADF activities, datasets, linked services, triggers, parameters, variables, expressions, and dependencies. Implement scheduled, incremental, and event-driven data integrations. Configure source and target connections using established enterprise patterns. Implement transformations and validation rules based on approved integration designs. Support integration pipelines connecting applications such as Salesforce, NetSuite, UKG Ready, Nulogy, and the Enterprise Data Warehouse. ETL/ELT & Data Integration Develop robust ETL/ELT processes for moving data between enterprise systems. Translate approved source-to-target mappings into production-ready pipelines. Implement field-level transformations, business-key handling, and data validation. Support staging, transformation, and target-loading processes. Ensure integrations follow documented business rules and data ownership requirements. Work safely within established enterprise integration and data-processing frameworks. SQL & Data Validation Develop and maintain SQL/T-SQL queries for data transformation, validation, reconciliation, and troubleshooting. Use joins, aggregations, CTEs, window functions, and stored procedures where appropriate. Perform source-to-target data comparisons. Investigate missing, duplicate, invalid, or inconsistent records. Support reconciliation between source systems, staging areas, EDW targets, and downstream applications. API & Application Integration Develop and support integrations using REST APIs and JSON. Work with HTTP methods, authentication, API parameters, pagination, rate limits, and status/error codes. Implement API-based extraction and transmission processes. Handle request/response logging and controlled retry behavior. Support incremental API extraction and external/business identifier management. Troubleshoot API connectivity and application-integration failures. Error Handling & Data Controls Implement standard exception handling and retry mechanisms. Ensure failed records are appropriately logged and routed for resolution. Support duplicate prevention and idempotent processing. Implement incremental loading and change-detection mechanisms. Handle outlier and invalid records according to approved integration rules. Maintain auditability of pipeline execution, record counts, failures, and processing status. Support safe restart and reprocessing of failed integrations. The integration architecture specifically identifies exception handling, retry processing, monitoring, alerting, SLA monitoring, interface documentation, and integration health monitoring as core requirements. Monitoring & Production Support Monitor production ADF pipelines and integration health. Investigate failed pipeline runs and activity-level errors. Identify whether failures originate from source data, transformation logic, authentication, connectivity, APIs, or target-system validation. Perform controlled restart and reprocessing activities. Monitor integration SLAs and processing performance. Maintain operational documentation and incident-resolution records. Communicate integration issues and business impacts to relevant stakeholders. Deployment & Documentation Use Git/source control and established deployment practices. Participate in development, testing, and production release processes. Document interfaces, mappings, pipeline dependencies, and operational procedures. Follow established development and governance standards. Support knowledge transfer and maintain accurate integration documentation. Technology Requirements Must Have Azure Data Factory ADF Pipelines & Activities ADF Linked Services & Datasets ADF Triggers, Scheduling, Parameters & Variables SQL / T-SQL ETL/ELT Data transformation and source-to-target mapping Exception handling and retry processing Pipeline monitoring and alerting Data validation and reconciliation Strongly Preferred REST APIs JSON API authentication and integration patterns CSV / flat-file integrations Azure security and authentication concepts Git / source control CI/CD and deployment practices Enterprise Data Warehouse concepts Nice to Have Salesforce integration experience NetSuite integration experience UKG Ready integration experience Nulogy or manufacturing/operations-system integration experience Power BI awareness Advanced Power BI/DAX is not a requirement for this role; Power BI knowledge is primarily needed to understand downstream reporting impact. Required Qualifications 3–6 years of experience in data integration, ETL/ELT, data engineering, or a related technical field. Strong hands-on experience with Azure Data Factory. Strong SQL/T-SQL development and troubleshooting skills. Experience developing production-grade data pipelines. Experience with source-to-target data mapping and transformation. Experience implementing data validation and reconciliation. Experience with pipeline monitoring, error handling, retries, and production support. Practical experience working with REST APIs and JSON. Experience with Git/source control and structured deployment processes. Strong problem-solving and troubleshooting skills. Ability to work from approved technical designs and independently deliver integration solutions. Cross-System Data Knowledge The developer should have, or be able to quickly develop, working knowledge of the data interfaces for: System Expected Understanding Salesforce Objects/records, external IDs, required fields, statuses, Product/Sales Order relationships NetSuite Transactions, items, financial classifications, external IDs, validation failures, accounting periods UKG Ready Employees, time, payroll/labor attributes, department/class/segment codes Nulogy Receipts, consumption, finished goods, shipments, invoices, inventory adjustments Enterprise Data Warehouse Staging, loading, business keys, incremental processing, data quality Power BI Understanding of how failed or stale pipelines affect downstream reporting The role does not require administration of Salesforce, NetSuite, UKG, or Nulogy. The developer needs sufficient data-interface knowledge to build and troubleshoot integrations with those systems. What “Mid-Level” Means A successful mid-level developer should be able to independently: Build a pipeline from an approved source-to-target mapping. Configure source and target connections using established patterns. Implement transformations and validations. Implement standard exception handling and retry behavior. Troubleshoot pipeline failures. Write SQL validation and reconciliation queries. Deploy using the established release process. Document interfaces and pipeline behavior. Support production integrations. The developer is not expected to independently: Define enterprise integration architecture. Select new integration platforms. Define systems of record. Establish enterprise financial rules. Own enterprise security architecture. Independently change approved source-to-target mappings. These responsibilities remain with architecture, data owners, platform owners, and governance. Core Competencies Competency Expected Level Azure Data Factory Strong SQL / T-SQL Strong ETL / ELT Strong Data Mapping / Transformation Strong Error Handling / Retries Strong Monitoring / Production Support Strong REST APIs / JSON Intermediate–Strong Git / CI-CD Intermediate Data Warehouse Concepts Intermediate Azure Security / Authentication Intermediate Salesforce / NetSuite / UKG Data Working Knowledge Power BI Basic–Intermediate Awareness Enterprise Architecture Working Knowledge – Not Ownership Key Success Measures Success in this role will be demonstrated by: Reliable and timely execution of enterprise data pipelines. Low recurrence of integration failures through effective root-cause analysis. Accurate source-to-target data movement. Effective handling of rejected, failed, and outlier records. Proper duplicate prevention and reprocessing controls. Strong pipeline monitoring and alerting. Accurate reconciliation of source and target record counts. Complete and maintainable integration documentation. Minimal disruption to downstream EDW and reporting processes. Ideal Candidate Profile The ideal candidate is a hands-on ADF developer who combines Azure Data Factory + SQL + APIs + ETL/ELT + production support capabilities. They should be comfortable taking an approved integration design and turning it into a reliable production pipeline without requiring step-by-step guidance from a senior engineer. They should also understand that enterprise integration is more than moving data from one system to another—it requires validation, idempotency, change detection, exception handling, retry processing, monitoring, reconciliation, auditability, and awareness of downstream business impact.