{"id":4223592,"url":"https://alion.io/job/international-paper-data-engineer-iii","title":"Data Engineer III","company":{"id":1807304,"name":"International Paper","domain":"internationalpaper.com","url":"https://alion.io/company/internationalpaper","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":83,"open_postings":73,"ghost_share":0,"stale_share":0.644,"repost_share":0.055,"time_to_fill_p50_days":27,"computed_at":"2026-11-12T10:38:37Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Atlanta, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":83000,"max":121000,"currency":"USD","period":"year","gross":null,"usd_annual":121000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Configuration Management","optional":false},{"name":"Delta Lake","optional":false},{"name":"Git","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false}],"status":"live","first_seen_at":"2026-11-12T20:16:09Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-13T06:44:19Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Position Title:\nData Engineer III\nPay Rate:\n$83,000 - $121,000\nMultiple factors, including Individual experience, skills and abilities will determine where an employee is ultimately placed in the pay range.\nCategory/Shift:\nSalaried Full-Time\nPhysical Location:\n75 5th Street NW\nAtlanta, GA 30308\nUnited States\nThe Job You Will Perform:\nThe Data Engineer is an important member of the Cost Innovation Team, which is a strategic, innovative, and cross-functional team that is focused on creating value for the company through the advanced analytics of manufacturing data. This position offers a flexible working arrangement provided that the home office is within a typical commuting distance to the A2C office.\nCore Responsibilities\nAnalytics & Data Science Enablement\nPartner with analysts, data scientists, engineers, and business stakeholders to translate functional requirements into scalable technical data solutions.\n\nDeliver self-service, analytics-ready datasets that empower downstream teams to consume data without deep knowledge of underlying source systems.\n\nEnable industrial analytics use cases by providing trusted, contextualized time-series, asset, event, and operational data for analytics platforms and applications.\n\nEliminate redundant data-preparation and contextualization efforts across disparate analytics and application teams.\n\nHelp establish standard data definitions, reusable data products, and consistent business context across enterprise and industrial analytics environments.\n\nSupport downstream analytics workflows, including advanced analytics, data science, visualization, and industrial process analytics.\n\nData Ingestion & Integration\nDesign, build, and maintain robust pipelines to ingest data from relational databases, operational systems, industrial historians, APIs, cloud platforms, and flat files.\n\nEstablish reliable and secure connections between source systems, cloud data platforms, and industrial data platforms.\n\nWrite optimized SQL queries and extraction logic for high-performance data retrieval.\n\nImplement flexible ingestion patterns including batch, incremental, streaming, and replication-based approaches based on business latency requirements.\n\nIntegrate high-volume time-series data and industrial metadata while maintaining relationships between assets, measurements, events, and other operational entities.\n\nSupport ingestion and integration patterns used by platforms such as Cognite Data Fusion (CDF), Seeq, and Microsoft Fabric.\n\nData Storage & Lakehouse Engineering\nDesign and manage scalable storage structures across raw (bronze), intermediate (silver), and curated (gold) layers.\n\nLeverage Spark-native formats and engines such as Delta Lake to support scalable analytics workloads.\n\nDesign curated data structures that support both enterprise analytics and industrial data consumption.\n\nEnforce data retention, archiving, and purging policies to manage data lifecycles.\n\nMonitor and optimize storage, compute costs, and cloud resource utilization.\n\nData Transformation\nDevelop Python and SQL-based transformations to clean, standardize, enrich, and unify data.\n\nDevelop, tune, and proactively optimize scalable PySpark applications to efficiently process high-volume datasets.\n\nBuild modular, reusable transformation components rather than siloed, application-specific logic.\n\nResolve complex data-quality anomalies including schema drift, missing values, duplicates, timestamp alignment, and inconsistent identifiers.\n\nEngineer efficient incremental processing patterns to handle high-volume, frequently changing datasets.\n\nTransform raw operational and historian data into analytics-ready time series, calculations, conditions, events, and contextualized datasets for downstream consumption.\n\nEnable reusable data transformations that can support platforms such as Seeq, Cognite, Fabric, and custom analytics applications.\n\nSemantic Modeling & Industrial Data Contextualization\nTranslate complex source schemas into intuitive, business-aligned semantic entities, asset hierarchies, and industrial data models.\n\nBuild and maintain relationships between assets, equipment, time series, events, files, and other operational data.\n\nStandardize naming conventions, identifiers, metadata, and relationship mappings across source systems.\n\nPerform data contextualization to connect operational data with relevant equipment, process, and business context.\n\nSupport asset-centric and knowledge-graph-style data models commonly used in industrial data platforms such as Cognite.\n\nPipeline Orchestration & Operations\nArchitect end-to-end workflows that seamlessly coordinate ingestion, transformation, contextualization, validation, and publication.\n\nOrchestrate data movement between source systems, lakehouse environments, industrial data platforms, and analytics applications.\n\nImplement resilient error handling including retries, automated recovery, checkpointing, and alerting.\n\nMonitor pipeline health, SLA compliance, data freshness, and data drift using centralized observability tools.\n\nDesign pipelines capable of supporting both traditional enterprise data and high-frequency industrial time-series workloads.\n\nData Governance, Security & DevOps\nEnforce data security and privacy standards, including row- and column-level security, RBAC (Role-Based Access Control), and masking of sensitive data.\n\nApply consistent access-control and governance patterns across enterprise data platforms and industrial analytics environments.\n\nEmbed automated validation checks as needed to enforce data quality.\n\nMaintain Git-based development workflows using branching, code reviews, and pull requests.\n\nImplement automated CI/CD pipelines for code testing, data validation, configuration management, and repeatable environment deployments.\n\nSupport deployment and lifecycle management of data pipelines, transformation logic, semantic models, and analytics configurations across development, test, and production environments.\n\nMaintain comprehensive documentation of data architectures, pipeline schemas, source-to-target mappings, contextual relationships, and lineage maps.\n\nPreferred Experience\nBachelor’s Degree in Computer Science, Software Engineering, Data Science, Engineering, or related field + 2 years of relevant experience; equivalent Master’s degree accepted; non-relevant bachelor’s degree requires 5+ years of equivalent hands-on experience.\n\nExperience working with:\nSQL 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