Data Engineer III

Job-:
Atlanta, GA, United States

Schedule:

Full time, Day Posted:
Closes:

Position Title:

Data Engineer IIIPay Rate: $83,000 - $121,000

Multiple factors, including Individual experience, skills and abilities will determine where an employee is ultimately placed in the pay range.

Category/Shift: Salaried Full-Time Physical

Location:

75 5th Street NW Atlanta, GA 30308 United States The Job You Will Perform:
The 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. Core Responsibilities Analytics & Data Science Enablement Partner with analysts, data scientists, engineers, and business stakeholders to translate functional requirements into scalable technical data solutions.

Deliver self-service, analytics-ready datasets that empower downstream teams to consume data without deep knowledge of underlying source systems.

Enable industrial analytics use cases by providing trusted, contextualized time-series, asset, event, and operational data for analytics platforms and applications.

Eliminate redundant data-preparation and contextualization efforts across disparate analytics and application teams.

Help establish standard data definitions, reusable data products, and consistent business context across enterprise and industrial analytics environments.

Support downstream analytics workflows, including advanced analytics, data science, visualization, and industrial process analytics.

Data Ingestion & Integration Design, build, and maintain robust pipelines to ingest data from relational 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 for high-performance data retrieval.

Implement flexible ingestion patterns including batch, incremental, streaming, and replication-based approaches based on business latency requirements.

Integrate high-volume time-series data and industrial metadata while maintaining relationships between assets, measurements, events, and other operational entities.

Support ingestion and integration patterns used by platforms such as Cognite Data Fusion (CDF), Seeq, and Microsoft Fabric.

Data Storage & Lakehouse Engineering Design and manage scalable storage structures across raw (bronze), intermediate (silver), and curated (gold) layers.

Leverage Spark-native formats and engines such as Delta Lake to support scalable analytics workloads.

Design curated data structures that support both enterprise analytics and industrial data consumption.

Enforce data retention, archiving, and purging policies to manage data life cycles.

Monitor and optimize storage, compute costs, and cloud resource utilization.

Data Transformation Develop Python and SQL-based transformations to clean, standardize, enrich, and unify data.

Develop, tune, and proactively optimize scalable PySpark applications to efficiently process high-volume datasets.

Build modular, reusable transformation components rather than siloed, application-specific logic.

Resolve complex data-quality anomalies including schema drift, missing values, duplicates, timestamp alignment, and inconsistent identifiers.

Engineer efficient incremental processing patterns to handle high-volume, frequently changing datasets.

Transform raw operational and historian data into analytics-ready time series, calculations, conditions, events, and contextualized…