• Build extraction pipelines from SAP HANA to AWS S3 using SLT, ODP, CDS views, SDI, and native HANA SQLScript, picking the right tool per source and per latency requirement. • Model raw SAP tables across FI/CO, MM, SD, and adjacent modules into clean, semantically meaningful datasets that the downstream Spark layer and business users can actually use. • Design and operate delta and CDC patterns so incremental loads stay correct, idempotent, and replayable. • Write ABAP extractors where standard SAP tooling falls short, and document them so future engineers can change them safely. • Own the write-back path: load curated data from S3 into SAP BW / BW4HANA and model it for end-user reporting and analytical querying. • Land data in S3 as Parquet with sane partitioning, schemas, and IAM scoping, and define the contract with the PySpark engineer at the ingestion-to-transformation boundary. • Embed with a customer team, ship the pipeline to production, and stay close enough through cutover to know it actually runs.

• Help with optimization, support and deployment of our cloud-based data systems • Build secure, scalable, and compliant database solutions using Azure SQL Server, and other cloud-native technologies • Support the evolution of our analytics lakehouse and all of its data sources • Ensure platform supports real-time clinical workflows, patient data analytics, and regulatory reporting

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.