Data Engineer III

International Paper•Atlanta, GA

19h

About The Position

The Data Engineer III is an important member of the Cost Innovation Team, a strategic, innovative, and cross-functional group focused on creating value through advanced analytics of manufacturing data. The position offers a flexible working arrangement, provided the home office is within a typical commuting distance of the A2C office. The role builds and operates data solutions that enable enterprise and industrial analytics, including data ingestion and integration, lakehouse storage, transformations, semantic modeling, orchestration, governance, security, and DevOps.

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.

Nice To Haves

Experience with platforms such as Cognite Data Fusion (CDF), Seeq, and Microsoft Fabric.
Experience with Delta Lake and Spark-native formats.
Experience with industrial data platforms, asset-centric data models, or knowledge-graph-style models.

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 time-series, asset, event, and operational data for downstream analytics.
Eliminate redundant data-preparation and contextualization work and help establish standard data definitions, reusable data products, and consistent business context.
Design, build, and maintain pipelines that ingest data from databases, operational systems, industrial historians, APIs, cloud platforms, and flat files.
Establish reliable and secure connections between source systems, cloud data platforms, and industrial data platforms; write optimized SQL queries and extraction logic.
Implement batch, incremental, streaming, and replication-based ingestion patterns according to business 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 (bronze), intermediate (silver), and curated (gold) storage structures, using Spark-native formats and engines such as Delta Lake.
Design curated data structures for enterprise analytics and industrial data consumption; enforce retention, archiving, and purging policies and optimize storage, compute costs, and cloud resource utilization.
Develop Python- and SQL-based transformations to clean, standardize, enrich, and unify data; develop and optimize scalable PySpark applications.
Build modular, reusable transformation components and efficient incremental processing patterns for high-volume, frequently changing datasets.
Resolve data-quality issues such as schema drift, missing values, duplicates, timestamp alignment, and inconsistent identifiers.
Transform raw 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; maintain relationships among assets, equipment, time series, events, files, and other operational data.
Standardize naming conventions, identifiers, metadata, and relationship mappings, and contextualize operational data with equipment, process, and business context.
Architect and orchestrate end-to-end workflows for ingestion, transformation, contextualization, validation, and publication across platforms and analytics applications.
Implement resilient error handling, including retries, automated recovery, checkpointing, and alerting; monitor pipeline health, SLA compliance, data freshness, and data drift.
Enforce security and privacy standards, including row- and column-level security, RBAC, and masking of sensitive data, and apply consistent access-control and governance patterns.
Embed automated data-validation checks as needed; maintain Git-based workflows with branching, code reviews, and pull requests.
Implement automated CI/CD pipelines for code testing, data validation, configuration management, and repeatable environment deployments.
Support deployment and lifecycle management of pipelines, transformation logic, semantic models, and analytics configurations across development, test, and production.
Document data architectures, pipeline schemas, source-to-target mappings, contextual relationships, and lineage maps.