Data Engineer III

International Paper•Atlanta, GA

1d•$83,000 - $121,000

About The Position

The Data Engineer III is an important member of International Paper’s Cost Innovation Team, a strategic, innovative, cross-functional team that creates value for the company through advanced analytics of manufacturing data. The role partners with analysts, data scientists, engineers, and business stakeholders to provide scalable technical data solutions and analytics-ready datasets. It enables industrial analytics by supplying trusted, contextualized time-series, asset, event, and operational data, and helps establish consistent data definitions, reusable data products, and business context across enterprise and industrial analytics environments. The position designs and maintains data ingestion and integration pipelines for relational databases, operational systems, industrial historians, APIs, cloud platforms, and files. It engineers lakehouse storage and transformations across raw, intermediate, and curated layers; develops Python, SQL, and PySpark processing; contextualizes industrial data through semantic models and asset relationships; and orchestrates reliable workflows across data platforms and analytics applications. The role also supports data governance, security, quality, observability, documentation, and CI/CD practices. A flexible working arrangement is available provided the home office is within typical commuting distance of the A2C office.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Science, Engineering, or a related field, plus 2 years of relevant experience; an equivalent master’s degree is accepted.
A non-relevant bachelor’s degree requires 5 or more years of equivalent hands-on experience.
Experience with SQL and Python for data engineering and transformation.
Experience with PySpark and Microsoft Fabric for lakehouse transformations.

Responsibilities

Partner with analysts, data scientists, engineers, and business stakeholders to translate functional requirements into scalable data solutions.
Deliver self-service, analytics-ready datasets and trusted, contextualized operational data for downstream analytics and applications.
Reduce redundant data-preparation and contextualization work and help establish standard definitions, reusable data products, and consistent business context.
Design, build, and maintain ingestion pipelines for relational databases, operational systems, industrial historians, APIs, cloud platforms, and flat files.
Establish secure and reliable connections between source systems, cloud data platforms, and industrial data platforms; write optimized SQL extraction logic.
Implement batch, incremental, streaming, and replication-based ingestion patterns based on latency requirements.
Integrate high-volume time-series data and industrial metadata while preserving relationships among assets, measurements, events, and operational entities.
Design and manage scalable raw, intermediate, and curated storage structures, including use of Spark-native formats and Delta Lake.
Design curated data structures, enforce data-retention and lifecycle policies, and monitor and optimize storage, compute costs, and cloud resource use.
Develop Python- and SQL-based transformations and scalable, optimized PySpark applications to clean, standardize, enrich, and unify data.
Build reusable transformation components and resolve data-quality issues such as schema drift, missing values, duplicates, timestamp alignment, and inconsistent identifiers.
Engineer incremental processing and transform operational and historian data into analytics-ready time series, calculations, conditions, events, and contextualized datasets.
Translate source schemas into business-aligned semantic entities, asset hierarchies, and industrial data models; standardize identifiers, metadata, naming, and relationships.
Contextualize operational data with equipment, process, and business information, supporting asset-centric and knowledge-graph-style models.
Architect and orchestrate workflows for ingestion, transformation, contextualization, validation, and publication across data platforms and applications.
Implement resilient pipeline operations, including retries, recovery, checkpointing, alerting, and monitoring of pipeline health, SLAs, freshness, and data drift.
Enforce data security and privacy controls, including row- and column-level security, RBAC, masking, and consistent access-control and governance patterns.
Embed automated data-validation checks; maintain Git-based development workflows and implement CI/CD for testing, validation, configuration, and repeatable deployments.
Support lifecycle management of pipelines, transformations, semantic models, and analytics configurations across development, test, and production.
Document data architectures, pipeline schemas, source-to-target mappings, contextual relationships, and lineage maps.