Top Data Engineer Jobs in Charlotte, NC

Design and develop data pipelines, integrations, transformations, and infrastructure across AWS, Snowflake, Airflow, and MySQL. Troubleshoot warehouse issues, validate data quality, improve pipeline reliability, and translate business needs into data engineering solutions. Interpret technical findings for client recommendations, manage workstreams, review junior team members’ work, and provide technical guidance across managed services engagements.

Leads enterprise-scale data engineering, analytics, governance, and AI initiatives for Corporate and Investment Banking Finance. Designs scalable architectures, pipelines, curated datasets, semantic models, and data products; modernizes reporting through cloud platforms and automation; establishes data quality, lineage, metadata, and governance standards; and drives responsible generative AI adoption. Partners with senior Finance, Technology, Data Management, and business stakeholders while leading cross-functional data transformation programs.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Designs, develops, validates, and documents analytics data solutions for diverse client environments. Builds Snowflake, SQL, dbt, and AWS ingestion and transformation pipelines; monitors data quality and performance; troubleshoots complex technology issues; and supports reporting and business insights. Manages project scope, budgets, documentation, governance, client communication, implementation planning, and quality assurance while collaborating across teams and identifying opportunities for additional solutions.

Build and maintain automated data quality testing for customer data pipelines. Responsibilities include developing dbt, Soda, and SQL checks; validating schemas, loads, sequencing, and downstream reconciliations; detecting anomalies; managing severity levels and customer overrides; reporting quality metrics; and partnering with engineering teams to embed testing into new pipelines. The role requires strong backend testing, SQL, Python, Airflow, and Databricks experience, along with excellent debugging and communication skills.

Lead operations and technical delivery for Wells Fargo’s on-premises Hortonworks big data platform. Responsibilities include designing, coding, testing, debugging, documenting, deploying upgrades, supporting tenant compute and storage, troubleshooting data-processing services, monitoring platform health, and leading migration activities to new data centers. The role provides end-to-end production support, escalates complex issues, guides junior staff, and coordinates initiatives across risk data environments.

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Develop and automate data workflows, analytical tools, visualizations, and AI/ML capabilities supporting engineering, product design, sales, and manufacturing. Translate business requirements into automated solutions, monitor production datasets, improve analytics workflows, collaborate with IT and cross-functional teams, and communicate analytics value to stakeholders. The role also provides data expertise and documentation across the organization.

Leads enterprise architecture and engineering standards for public-cloud data, analytics, AI/ML, and Agentic AI platforms. Responsibilities include modernizing legacy platforms, designing lakehouse and real-time ecosystems, establishing governance and reliability standards, enabling self-service data products, and developing AI-agent automation. The role partners with security, risk, compliance, and engineering teams to deliver secure, resilient, observable, and cost-efficient cloud capabilities in a regulated enterprise environment.

Build and support scalable data pipelines, ETL/ELT processes, orchestration workflows, APIs, and cloud data services on Google Cloud Platform. Integrate enterprise platforms with governance systems for metadata discovery, lineage, classification, and policy enforcement. Develop secure, observable, cost-efficient solutions using CI/CD, testing, automation, DataOps, and DevSecOps practices, while contributing to AI-enabled workflows and reusable platform frameworks.

Leads global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Own and deliver highly available, low-latency backend identity systems supporting consumer onboarding, authentication, and verification. Lead quarterly team goals, guide engineers through ambiguous problems, collaborate with product and design partners, monitor system reliability, improve engineering standards, support on-call operations, and mentor team members. The role requires designing and launching distributed systems at scale using Python or Kotlin, AWS, MySQL, and Kubernetes.

Leads the modernization of enterprise data platforms supporting reporting, statements, and letters. Architects cloud-native data platforms, ETL/ELT pipelines, streaming solutions, and real-time processing using AWS, Snowflake, Python, dbt, Airflow, and Kafka. Establishes engineering standards, technical roadmaps, observability, data quality, CI/CD, and DevOps practices. Provides hands-on technical leadership, mentors engineers, manages delivery across internal and vendor teams, resolves production issues, and drives cloud transformation and platform reliability.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Own data pipelines into and out of the warehouse, dbt models, data integrity, governance, identity resolution, reverse ETL, and quality monitoring across growth, lifecycle, and brokerage data. Partner with product, marketing, and operations teams to establish data contracts, ensure accurate and timely business metrics, and support regulated financial data requirements.

Build and operate data pipelines ingesting and normalizing security data from multiple sources. Own entity resolution, asset attribution, data quality monitoring, automated correlation logic, and reporting support. Collaborate with security analysts and leadership to turn noisy data into actionable insights and improve remediation outcomes. Contribute to AI-augmented data analysis and correlation capabilities.

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).

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Serves as the senior technical authority for enterprise browser data protection and security. Owns architecture, roadmap, standards, lifecycle, browser-based DLP controls, integrations, automation, and operational support. Leads complex troubleshooting, major incident response, root-cause analysis, runbook development, and continuous improvement. Partners across cybersecurity, identity, endpoint, network, cloud, risk, compliance, and vendor teams while mentoring engineers and influencing enterprise security design.

Design and build scalable data infrastructure, including lakehouse architecture, ClickHouse deployments, ETL/ELT pipelines, and data models connecting Postgres with analytics systems. Establish data quality, observability, performance, cost, and reliability standards. Collaborate with product and engineering teams, define data contracts and specifications, support real-time analytics, and use AI-assisted coding and LLMs to accelerate pipeline development and transformation.

Lead design and delivery of cloud-native, AI-powered data platforms, copilots, and enterprise-scale services. Architect LLM/RAG/agentic solutions, build AI-ready data foundations, create reusable frameworks/SDKs, establish Responsible AI and governance practices, and provide hands-on technical leadership and cross-team partnership to drive AI adoption and scale.

Supports ETL pipeline implementation, data ingestion, data quality validation, schema and RBAC configuration, database performance testing, interoperability testing, and production migration activities. Maintains metadata registries, documents ingestion defects and remediation recommendations, validates post-migration data, and maintains database scripts and pipeline code in version control. The position is fully remote and supports a federal data migration program.

Develop and deploy Databricks Delta Live Tables ETL pipelines in a cloud lakehouse environment. Aggregate, normalize, transform, and validate geospatial data from authoritative sources. Document ingestion results, data quality defects, remediation actions, and test outcomes. Maintain version-controlled code with peer reviews, support GIS-based spatial validation, and assist with interoperability testing. The role supports federal endangered-species data integration and requires proficiency in Python or Scala, SQL, data quality validation, schema documentation, and geospatial data concepts.

Build a high-performance frontend data platform for exploring and analyzing hardware telemetry. Develop React and TypeScript applications, interactive time-series visualizations, data visualization plugins, complex application state management, and LLM-assisted analysis workflows. Collaborate directly with hardware engineers and customers to define interfaces, prototype workflows, demonstrate features, and incorporate feedback into product development.