Analytics Engineer

• Collects, transforms, and validates data from diverse sources, applying professional analytical techniques to ensure accuracy and reliability.
• Performs exploratory and descriptive analysis to identify trends, patterns, and key performance drivers for business stakeholders.
• Develops and maintains reports, dashboards, and visualizations that communicate analytical findings in a clear and actionable manner.
• Interprets data requirements, collaborates with business partners, and applies professional judgment to structure analyses that meet defined objectives.
• Documents analytical logic, data sources, and methodologies to support transparency, reproducibility, and continuous improvement.
• Supports cross-functional initiatives by providing data-driven insights and contributing to analytical problem-solving efforts.
• Identifies and recommends opportunities to streamline analytical workflows, standardize reporting, or enhance data quality.
• Builds proficiency in analytics tools, data modeling concepts, and best practices to expand analytical independence and effectiveness.

Data Analytics Engineer

• Consult with teams across the organization to translate key product and business processes into technical data requirements. Use dbt, BigQuery, and SQL to build up Packback’s metrics layer to enable self-serve access to core analytics.
• Build, optimize, and orchestrate data pipelines with dbt, Airflow, and Python
• Establish data integrity standards and SLAs to ensure timely, accurate delivery of data
• Build insightful and reliable dashboards to track performance of core company metrics across the product and business
• Collaborate with business operations teams to define and train on best practices with respect to data modeling, analytics, and visualization
• Collaborate with leadership to conduct analyses and apply statistical methods to answer day-to-day questions or support larger initiatives
• Work within Packback’s research function with partner institutions and perform in-depth analyses to understand how Packback’s products are driving key student outcomes
• Consult on the adoption of new technologies that impact Packback’s data infrastructure

• Apply advanced statistical theory to quantify, analyze, and manage data, financial risks, and market dynamics through robust modeling approaches
• Collaborate and partner with internal and external stakeholders to gather requirements and deliver impactful, data driven and AI enabled solutions
• Engage with Audit, Model, Product, and technical stakeholders to ensure transparency, governance, and compliance with regulatory expectations
• Establish and promote best practices across the model lifecycle, including development, evaluation, validation, deployment, and monitoring, while contributing to analytical strategies, modeling approaches, and forecasting methodologies through execution of complex model development and implementation
• Drive innovation through proof-of-concept initiatives, experimentation, and applied research, while staying current on emerging trends in Generative AI, large language models (LLMs), and Natural Language Processing (NLP), and translating insights into practical business applications
• Guide and mentor junior team members, fostering technical excellence, collaboration, and knowledge sharing
• Lead the design, architecture, and implementation of scalable artificial intelligence (AI), machine learning (ML), Natural Language Processing (NLP), and large language model (LLM) solutions using advanced techniques, modern frameworks, and cloud native tools to support complex language understanding use cases

Senior Quantitative Analytics Specialist – Senior GenAI Data Scientist

• Design, implement, and support end‑to‑end ELT pipelines (ingest → transform → publish) in Databricks/ADF
• Implement data quality checks (DLT expectations, unit tests) with alerting and remediation runbooks
• Build curated, analytics‑ready Delta tables using dimensional modeling for consumption by BI Developers
• Implement CDC and deletion‑flag patterns; manage schema drift and partitioning/Z‑Ordering strategies
• Operationalize jobs with monitoring, logging, alerting; participate in an on‑call rotation as needed
• Partner with the Data Architect to align designs with standards for governance, security, and cost efficiency
• Document pipelines, data contracts, and SLAs; continuously improve performance and reliability

Data Solutions Engineer

• Develop and maintain ETL processes using Informatica PowerCenter and related components.
• Create, maintain, and optimize stored procedures, functions, inline SQL, and other database-driven processes.
• Monitor, support, and troubleshoot applications utilizing technologies such as Informatica and Greenplum.
• Work during production maintenance windows and participate in on‑call support as needed.
• Participate independently in all phases of the Agile Software Development Life Cycle.

ETL Developer

• Design and develop data serving and semantic layers to support business needs ranging from traditional reporting to reverse ETL and LLM/AI implementations.
• Develop data models for use by analysts and applications from across the business using modern dimensional modeling practices to efficiently serve accurate, consistent data.
• Build and maintain robust, scalable data pipelines to extract, transform, and load (ETL, ELT) data from various sources into the data platform using both batch processing and event-driven patterns.
• Create efficient, performant data models to support business requirements and analytics needs.
• Implement and automate data quality checks and monitoring to ensure data accuracy and drive observability.
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions and on-premises systems.
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis.
• Work closely with data analysts, data scientists, and business stakeholders to understand their data needs and deliver solutions.
• Identify and resolve data-related issues and challenges for technical and non-technical stakeholders.
• Support company-wide projects ranging from server migrations to mergers and acquisitions as a data and systems expert.
• Automate data processes to improve efficiency and reduce manual effort.
• Implement modern concepts like metadata-driven pipelines and Infrastructure-as-Code that will play a key role in a scalable, resilient data platform.
• Document data processes, architecture, and workflows.

• Collaborate with stakeholders to understand business requirements and translate them into scalable and efficient data analytics solutions.
• Possess in-depth knowledge and hands-on experience with Microsoft Fabric ecosystem (Lakehouse, PySpark/SQL notebooks, data pipelines, and deployment pipelines).
• Understanding Microsoft Dynamics as a data source a big plus.
• Design data models that bridge the Fabric Lakehouse, Power BI semantic models, and reporting layers, ensuring consistency between raw, curated, and business-facing data.
• Optimize data models to support efficient data storage, retrieval, and analysis, ensuring data integrity, accuracy, and reliability.
• Knowledge of Fabric capacity (CU) optimization a big plus.
• Implement data integration pipelines using Microsoft Fabric and other relevant tools to extract, transform, and load data from various sources into a unified data platform.
• Create visually appealing and interactive dashboards, reports, and visualizations using Microsoft Power BI and DAX language, enabling users to gain valuable insights from data.
• Identify performance bottlenecks and implement optimizations to improve data processing, query performance, and overall system efficiency.
• Collaborate with cross-functional teams, providing technical leadership and guidance to data engineers, analysts, and other stakeholders to ensure successful project delivery.
• Keep abreast of the latest advancements in data analytics technologies (Microsoft based and others) and industry trends, continuously enhancing your expertise and applying best practices in solution design and implementation.

• Develop a strong understanding of core electrical and mechanical system principles
• Conduct data analysis with Python to drive informed business decisions
• Apply statistical methods to assess data completeness, accuracy, and reliability
• Support engineering teams with root cause analysis investigations
• Promote the adoption of semantic models and ontologies across the organization
• Design and build executable Python-based analytical models
• Automate data processing, analysis, and formatting workflows
• Clean, structure, and analyze datasets using Python
• Organize data and implement standardized labeling practices in AWS S3
• Deploy machine learning algorithms on both numerical and categorical data sets

Analytics Engineer jobs in North Carolina

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Data Analytics Engineer

Analytics Engineer

Senior Quantitative Analytics Specialist – Senior GenAI Data Scientist

Data Solutions Engineer

ETL Developer

Analytics Engineer

Data Analytics Engineer

Senior Analytics Engineer