Role Description We are looking for a Data Integration Specialist to build, optimize, and maintain automated data pipelines for a remote business intelligence environment. This role ensures operational and financial data from multiple systems arrives clean, structured, and reliably for reporting and analytics. - Build and manage ETL/ELT pipelines using tools such as n8n, Microsoft Power Automate, and SQL. - Connect data sources through REST APIs, including OAuth authentication, JSON parsing, and webhook management. - Query, clean, and manipulate data using SQL, including PostgreSQL or T-SQL. - Prepare and transform complex datasets using advanced Power Query / M and Python before they feed into analytical models. - Configure, monitor, and troubleshoot Power BI Gateway connections, refresh schedules, and pipeline errors. - Create stable API integrations with third-party platforms such as ERP, POS, CRM, Excel, and SharePoint systems. - Establish and follow standards for folder structures, API logging, documentation, Git workflows, branches, and pull requests. - Document integration architecture, pipeline flows, data sources, and troubleshooting procedures. - Hands-on experience with automation and ETL tools such as n8n and/or Microsoft Power Automate. - Strong understanding of REST APIs, JSON, OAuth2, and webhooks. - Advanced Power Query / M language skills. - Intermediate Python / pandas experience for ETL and data transformation. - Advanced SQL skills and solid knowledge of relational databases. - Experience with version control, code reviews, and structured technical documentation. - Strong attention to data integrity, accuracy, and reliable pipeline performance. - Ability to independently solve unstructured data, integration, or connectivity issues with a systems-thinking mindset. Schedule - Monday to Friday, 8:00 AM – 12:00 PM (EST) - Fully remote work environment - Stable, long-term collaboration with potential to scale - Clear scope of responsibilities and structured workflows - Training and onboarding support - Opportunity to work with international teams - Exposure to modern BI, analytics, and financial reporting processes - Supportive and collaborative work culture - Opportunity for skill development and career growth Before You Apply, Please Read: - These are professional roles with set schedules defined by the client. - Consistent availability is expected. You will have a set work schedule determined by the client, and you are expected to be present during those hours. - You will be part of the client’s team. You’ll follow their company guidelines, communication practices, and workflows, ensuring seamless collaboration. - Your performance will be measured to ensure alignment with client expectations and project objectives. - Training may be required. You might need to complete training to fully understand your role, tools, and client processes. - Commitment and responsibility are essential. We work with clients who rely on your dedication to deliver consistent, high-quality results. This is key to building trust and long-term partnerships. - Ready to grow your career with us? Apply only if you are ready to commit, learn, and take ownership of your role.

Role Description We are seeking an experienced, hands-on Senior Director of Data Engineering & Transformation to lead the modernization, execution, and optimization of enterprise data platforms, architectures, and engineering capabilities across Lumen. This leader will drive scalable data products, modern pipelines, integration frameworks, and AI-ready data structures that enable analytics, artificial intelligence, business decision-making, and increased automation. In this role, the Senior Director will shape the company’s data landscape by modernizing platforms, improving engineering practices, and accelerating the organization’s ability to use data as a strategic asset. The position also focuses on making Lumen data more accessible and consumable for business users, enterprise applications, and AI agents to improve productivity and unlock greater business value, while ensuring data governance requirements such as quality, security, lineage, access controls, metadata, and compliance are embedded into technical solutions and architecture practices. Location This is a remote opportunity open to candidates located anywhere in the U.S. Main Responsibilities - Transform enterprise data infrastructure by modernizing data platforms, architectures, pipelines, and engineering practices. - Make data more available and consumable for business users, enterprise applications, analytics platforms, and artificial intelligence. - Design scalable enterprise data architectures that support analytics, AI, automation, reporting, and operational use cases. - Lead data pipeline development and optimization, including ETL/ELT, ingestion, transformation, orchestration, monitoring, testing, and production support. - Drive data platform modernization across cloud, lakehouse, integration, storage, analytics, and AI-enablement capabilities. - Deliver reusable data products and solutions, including APIs, semantic layers, governed access patterns, analytical datasets, and AI-ready data structures. - Enable AI-ready data architecture for machine learning, intelligent applications, AI agents, advanced analytics, and automation workflows. - Lead complex transformation initiatives from strategy through execution, including roadmaps, milestones, dependencies, risks, and measurable business outcomes. - Embed governance into technical delivery, including data quality, security, lineage, metadata, access controls, compliance, and responsible data use. - Lead and mentor technical teams across data architecture, data engineering, platform engineering, and technical delivery while fostering excellence, accountability, and innovation. Qualifications - Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related field. Advanced degree preferred. - 15+ years of experience in data engineering, data architecture, enterprise data platforms, data integration, data transformation, or related technical disciplines. - Strong experience designing and implementing large-scale data architectures, modern data platforms, enterprise data pipelines, and data engineering solutions. - Deep knowledge of ETL/ELT pipelines, data ingestion, data transformation, data modeling, data warehousing, lakehouse architectures, cloud data platforms, and distributed data systems. - Experience structuring data for analytics, reporting, AI, machine learning, business intelligence, intelligent applications, and automation use cases, including applied AI for data and AI-agent consumption patterns. - Strong understanding of data quality, security, metadata, lineage, access management, and governance requirements as part of technical delivery. - Proven ability to lead technical teams and manage complex data engineering, architecture, and transformation programs. - Strong communication skills with the ability to translate technical concepts into executive-level presentations, transformation roadmaps, and business-facing recommendations. - Experience evaluating, selecting, and implementing data technologies, platforms, and engineering frameworks. Preferred Skills - Experience with data analytics, business intelligence, applied AI for data, AI agents, automation enablement, and machine learning enablement platforms. - Familiarity with cloud-based data platforms and services such as Azure, AWS, Google Cloud, Snowflake, Databricks, Microsoft Fabric, or similar technologies. - Experience with data mesh, lakehouse architecture, domain-oriented data products, metadata management, feature stores, vector databases, or AI-ready data platform design. - Experience with DevOps, DataOps, CI/CD, orchestration, monitoring, testing automation, and production support for data platforms and pipelines. - Certification in cloud architecture, data engineering, data architecture, or data management preferred. Compensation This information reflects the anticipated base salary range for this position based on current national data. Minimums and maximums may vary based on location. Individual pay is based on skills, experience and other relevant factors. - $174,876 - $233,168 in these states: AL, AR, AZ, FL, GA, IA, ID, IN, KS, KY, LA, ME, MO, MS, MT, ND, NE, NM, OH, OK, PA, SC, SD, TN, UT, VT, WI, WV, WY - $183,621 - $244,827 in these states: CO, HI, MI, MN, NC, NH, NV, OR, RI - $192,364 - $256,486 in these states: AK, CA, CT, DC, DE, IL, MA, MD, NJ, NY, TX, VA, WA Benefits - Lumen offers a comprehensive package featuring a broad range of Health, Life, Voluntary Lifestyle benefits and other perks that enhance your physical, mental, emotional and financial wellbeing.

• Partner with technical and non-technical stakeholders to translate business requirements into scalable, reliable, and maintainable data engineering solutions. • Design, develop, and maintain data integration solutions using Microsoft SQL Server, Azure Data Factory, Azure Databricks, Microsoft Fabric, and related Azure services. • Build, manage, and optimize ETL/ELT pipelines using Azure Data Factory and Microsoft Fabric Pipelines, including pipeline orchestration, parameterization, scheduling, monitoring, and error handling. • Develop and maintain data transformation logic using Azure Databricks notebooks and Microsoft Fabric notebooks, with a focus on performance, scalability, reusability, and operational reliability. • Design and implement data ingestion, transformation, validation, and delivery processes across structured, semi-structured, and external data sources. • Develop and optimize data integration processes utilizing Azure Data Factory, Azure Databricks, Azure Storage Accounts, Azure Data Lake Storage, Azure Key Vault, Microsoft Fabric Lakehouses, Warehouses, Pipelines, and Notebooks. • Build and maintain SQL-based data models, stored procedures, views, automation scripts, and database objects to support operational and analytical workloads. • Support the modernization and migration of legacy data integration processes from SSIS and on-premises platforms to cloud-based Azure and Microsoft Fabric architectures. • Design and implement reusable pipeline patterns, data quality checks, validation controls, logging, alerting, and performance monitoring processes. • Support new client onboarding efforts by designing and implementing reliable data ingestion, transformation, reconciliation, and validation workflows. • Troubleshoot and resolve data pipeline, data quality, performance, and integration issues in collaboration with teams across Intelligence & Analytics. • Design, develop, and maintain datasets, semantic models, and data structures that support reporting, analytics, and visualization solutions across Power BI, Excel, Microsoft Fabric, and other business intelligence platforms. • Contribute to data engineering standards, documentation, best practices, and reusable frameworks that improve scalability, maintainability, and operational efficiency. • Research, evaluate, and recommend emerging technologies and platform capabilities that improve data architecture, automation, performance, and reliability.

• Lead the design and evolution of our core data architecture across transactional, analytical, and long-term storage systems. • Own and improve PostgreSQL data design, schema structure, indexing strategy, query performance, relationships, constraints, and data integrity. • Design, review, and maintain ERDs and data models that clearly represent business logic, system behavior, and domain relationships. • Work closely with backend engineers to ensure application data models are scalable, consistent, and suitable for both operational and analytical use cases. • Build and improve data pipelines from production systems into analytics databases and reporting layers. • Design reliable approaches for data ingestion, replication, transformation, validation, rollups, aggregations, and historical storage. • Help define the right architecture for hot, warm, and cold data storage. • Support the design of analytics-friendly datasets for reporting, dashboards, product analytics, finance, compliance, and operational monitoring. • Help evolve our analytics database strategy, including how we use analytical stores, materialised views, denormalised tables, and aggregation layers. • Establish data modeling best practices across the engineering team. • Identify and fix data quality issues, including inconsistent schemas, duplicated logic, missing relationships, poor naming, and unreliable reporting sources. • Collaborate with backend, product, DevOps, and leadership teams to turn business requirements into clean, scalable data structures. • Define best practices for migrations, data retention, archival, backfills, and safe deletion of production data. • Mentor engineers on database design, ERDs, data modeling, and data pipeline thinking.