• Develop, maintain, monitor, and optimize data pipelines that move information from operational business systems into the company’s enterprise data platform and data lake.

• Design and maintain ETL and ELT processes using Azure Data Factory and related Azure data technologies.

• Manage and support the company’s Azure Data Lake environment, including data organization, storage structures, processing workflows, access, and overall platform reliability.

• Architect new data feeds and integrations from enterprise applications, APIs, databases, files, and other internal and external data sources.

• Develop processes for ingesting structured and semi-structured data including SQL data, APIs, JSON, CSV, flat files, and other common data formats.

• Troubleshoot data feed failures, pipeline errors, processing issues, data discrepancies, and other problems affecting the availability or accuracy of business data.

• Monitor and improve data pipeline performance, processing times, query performance, resource utilization, and overall data platform efficiency.

• Develop and maintain SQL queries, views, stored procedures, transformations, and reusable data structures used by reporting and business applications.

• Develop curated datasets and reporting views that provide Power BI and other analytics tools with consistent, reliable, and understandable business data.

• Work with Power BI developers and data analysts to understand reporting requirements and translate business needs into appropriate data structures and models.

• Design and maintain data models that support enterprise reporting, analytics, operational reporting, historical analysis, and KPI measurement.

• Apply dimensional modeling principles where appropriate, including fact tables, dimension tables, relationships, measures, historical data, and common business entities.

• Improve business visibility into data by identifying opportunities to make information easier to access, understand, analyze, and use for decision-making.

• Establish and maintain appropriate data quality controls, validation processes, reconciliation procedures, and monitoring to identify missing, inaccurate, duplicated, or inconsistent data.

• Develop processes for incremental data loading, change detection, historical data retention, and efficient processing of large data sets.

• Establish standards for data naming, definitions, structure, documentation, ownership, lineage, and appropriate use across enterprise reporting.

• Support data governance initiatives that create consistent definitions and trusted sources for customers, employees, shipments, financial information, operational metrics, and other key business entities.

• Help establish consistent enterprise KPIs and reporting definitions to reduce conflicting calculations and different versions of the same business metric.

• Document data sources, pipelines, transformations, dependencies, business rules, data models, and reporting structures.

• Maintain visibility into dependencies between source systems, data pipelines, transformations, reporting datasets, and downstream applications.

• Participate in the evaluation and implementation of new data technologies, tools, integrations, and architectural improvements.

• Work with application developers, infrastructure teams, data analysts, project managers, and business stakeholders to support new projects and data requirements.

• Follow appropriate security, access control, data privacy, change management, testing, and development practices when working with enterprise data.

• Proactively identify opportunities to improve data reliability, processing performance, automation, scalability, maintainability, and overall data architecture.

• 3+ years of experience in data engineering, data integration, database development, business intelligence engineering, or a related technical role.

• Strong experience with Microsoft Azure data technologies, particularly Azure Data Factory, Azure Data Lake Storage, and DataBricks.

• Strong SQL skills with experience developing complex queries, views, stored procedures, transformations, and reporting datasets.

• Experience designing, developing, and maintaining ETL and ELT pipelines that integrate multiple enterprise data sources.

• Experience integrating data through REST APIs, databases, file transfers, JSON, CSV, flat files, and other common integration methods.

• Strong understanding of relational databases, data structures, database relationships, data types, indexing, and query optimization.

• Understanding of data warehousing concepts, dimensional modeling, fact and dimension tables, star schemas, and data structures designed for analytics.

• Experience preparing and modeling data for Power BI or similar business intelligence and visualization platforms.

• Understanding of Power BI data requirements, semantic models, relationships, refresh processes, and reporting performance considerations.

• Experience troubleshooting and optimizing data pipelines, SQL queries, transformations, and large data processing workloads.

• Knowledge of incremental loading, change data capture concepts, data synchronization, data dependencies, and historical data management.

• Experience implementing data validation, reconciliation, monitoring, logging, error handling, and data quality processes.

• Understanding of data governance concepts including data ownership, business definitions, lineage, documentation, access controls, quality, and trusted data sources.

• Ability to understand business processes and translate business reporting requirements into scalable technical data solutions.

• Ability to analyze data discrepancies and determine whether problems originate within source systems, integrations, transformation logic, data models, or reporting layers.

• Experience with Python for data processing, automation, integration, or data engineering is required.

• Familiarity with Git or other source control and development lifecycle practices.

• Familiarity with Azure security, role-based access control, service accounts, credentials, and secure data integration practices.

• Experience working with data originating from ERP, CRM, transportation, logistics, financial, warehouse, customer service, or other enterprise business systems is preferred.

• Strong analytical, troubleshooting, communication, organization, documentation, and problem-solving skills.

• Ability to explain technical data concepts, issues, dependencies, and recommendations clearly to both technical and non-technical stakeholders.

• Ability to work independently while collaborating cross-functionally with application development, infrastructure, analytics, project management, and business teams.

• Ability to take ownership of critical data processes and proactively identify potential failures, performance issues, data quality concerns, and architectural improvements.

• Ability to manage multiple priorities in a fast-paced environment with changing business and reporting needs.

• Ability to read, write, and speak English fluently.

Education

• Bachelor’s degree in Computer Science, Software Engineering, or a related field; or equivalent practical experience