Top Data Engineer Jobs in Charlotte, NC

Leads enterprise software engineering initiatives supporting financial forecasting, planning, and reporting. Designs, codes, tests, documents, and supports Oracle Essbase 21c solutions and associated infrastructure. Develops ETL solutions using Python, PySpark, and object storage; manages platform capacity, availability, performance, security, middleware, and production issue resolution. Establishes engineering standards, mentors teams, resolves complex technical issues, and supports modernization toward cloud-based data engineering and analytics platforms.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Architects, deploys, and operates CrowdStrike’s data platform infrastructure and services. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and access controls, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires cloud, container orchestration, SQL, data modeling, compliance, and AI technology expertise, along with eligibility for CJIS clearance.

Leads data engineering initiatives for enterprise counterparty credit risk technology. Designs, codes, tests, debugs, documents, deploys, and supports scalable data solutions and reporting. Collaborates with risk, capital markets, and technology stakeholders; resolves technical challenges, leads projects, mentors staff, and develops dashboards using Python, SQL, Power BI, and related data platforms.

Leads the design, implementation, automation, integration, and support of enterprise DLP and data protection solutions. Maintains sensitive-data feeds and integrations, troubleshoots complex security issues, performs risk assessments, evaluates technologies, and provides technical guidance. The role also mentors engineers, drives automation and operational improvements, and responsibly applies AI-assisted development tools while ensuring security, compliance, privacy, and ethical standards.

Build and operate low-latency market data systems for institutional trading, including feed handlers, normalization pipelines, venue connectivity, and distribution services. Develop high-throughput services, improve reliability and performance through observability and incident response, participate in on-call support, and collaborate with engineering and product teams. The role requires production backend engineering experience, market data infrastructure expertise, Java or C++, messaging frameworks, and exchange connectivity protocols.

Define and govern enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, and AI use cases. Establish standards for data quality, lineage, access, cataloging, governance, observability, and SLAs. Architect AI-enabled workflows, resolve complex architecture issues, influence roadmaps, and mentor engineers through hands-on technical leadership. The role requires 15+ years of software, data engineering, or architecture experience and expertise in large-scale data platforms and modeling.

Designs, builds, and maintains enterprise data structures, pipelines, storage systems, frameworks, and microservices. Develops scalable solutions using big data technologies, prepares data assets for analytics and business use cases, and ensures data quality, integrity, security, and regulatory compliance. Partners with data scientists, modelers, technology teams, business stakeholders, and executives to translate needs into data solutions and support strategic decision-making.

Designs and maintains secure, scalable data pipelines for structured and unstructured sources. Builds segmented data architectures with access controls, classification, governance, lineage, and auditability for sensitive environments. Optimizes workflows across cloud and hybrid platforms, supports production systems, and partners with technical, business, compliance, and cybersecurity stakeholders. The role enables analytics, AI/ML, and digital products while ensuring data integrity, performance, observability, and resiliency.

Designs and maintains scalable data pipelines, warehouses, lakes, integration solutions, and analytics platforms. Develops AWS and Snowflake data solutions using Python, SQL, dbt, ETL/ELT, and Data Vault modeling. Supports replication, APIs, governance, metadata, lineage, and data quality across enterprise environments. Troubleshoots complex data issues and provides technical leadership while collaborating with engineering, architecture, analytics, and business teams.

Lead design, build, and deploy of large-scale data platforms for LLMs, RAG, and agentic AI systems. Hands-on coding, architecting fault-tolerant pipelines, establishing MLOps/DataOps best practices, mentoring engineers, and operationalizing research into production across Exabyte-scale distributed systems.

Designs and maintains cybersecurity telemetry and data pipelines for monitoring, detection, investigation, reporting, and risk analysis. Integrates logs, alerts, asset, identity, vulnerability, and other security data into analytics platforms. Defines data models, collection patterns, retention requirements, quality controls, and integrations while improving data completeness, normalization, timeliness, and correlation. Partners with security operations, engineering, data teams, and system owners to resolve telemetry gaps and strengthen visibility.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Designs, builds, and maintains scalable batch and streaming data pipelines in Databricks using Python, Spark, SQL, and Delta Lake. Supports SQL Server, SSIS, and SSRS environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, applies warehousing best practices, and partners with business and IT teams to deliver reporting, analytics, and AI/ML data solutions.

Build and maintain scalable ETL/ELT pipelines, integrate travel and customer data from internal and external systems, develop cloud data warehouse models, implement data quality monitoring, support real-time streaming, and ensure data privacy and regulatory compliance. Collaborate with analysts, data scientists, and product teams to deliver reliable datasets for personalization, forecasting, pricing, route optimization, and customer experience initiatives.

Design, build, and maintain scalable data pipelines, data models, and analytics solutions using Microsoft Fabric and Azure. Develop Lakehouse and Warehouse architectures, Fabric Notebooks with PySpark and Spark SQL, ETL/ELT workflows, and Power BI semantic models. Optimize performance, ensure data quality, security, governance, and regulatory compliance, and collaborate with analysts, data scientists, and business stakeholders to support business intelligence and advanced analytics.

Architects, develops, deploys, and optimizes enterprise-scale data platforms, schemas, pipelines, data models, and integrations. Leads SQL engineering, ETL/ELT frameworks, API ingestion, cloud-native solutions, database performance tuning, analytics, business intelligence, and data governance. Supports Power BI and other BI tools while providing technical leadership across data engineering initiatives. Knowledge of disputes, chargebacks, fraud operations, and case-management systems is preferred.

Design and scale batch and streaming data pipelines, ETL/ELT workflows, data models, warehouses, quality frameworks, dashboards, and AI/ML feature pipelines. Automate reporting and analytics while collaborating with product, engineering, and operations teams. The role also includes internal consulting practice participation, thought leadership, client case studies, and contribution to digital transformation initiatives.

Build and maintain Databricks data pipelines across bronze, silver, and gold layers. Ingest APIs, logs, billing exports, and reference data; implement attribution logic, governance, data quality monitoring, and cost optimization. Manage Unity Catalog permissions, lineage, refresh schedules, incremental processing, and CI/CD workflows while supporting multi-cloud storage and high-volume caller-identity data.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Build and maintain Databricks-based data platforms, including ingestion, transformation, storage, governance, data modeling, and serving pipelines. Establish medallion architecture standards, canonical data models, quality controls, lineage, schema evolution, and reliable batch or incremental processing. Improve pipeline observability, scalability, idempotency, and recoverability while moving curated data to systems such as ClickHouse. Collaborate across application and analytics teams to create durable, governed production datasets.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Develop and optimize enterprise Oracle SQL and PL/SQL solutions, including data models, database designs, APIs, ETL processes, batch jobs, and performance tuning. Collaborate with cross-functional teams to translate requirements into scalable implementations, support testing and CI/CD, troubleshoot production issues, and document solutions. Provide technical leadership, mentor engineers, participate in architecture and code reviews, and drive engineering best practices across data modernization initiatives.