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.

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.

Design, develop, and operate Lowe’s scalable Search Platform using OpenSearch and Elasticsearch. Build cloud-native services, REST APIs, and batch or near real-time data ingestion and ETL pipelines on Google Cloud. Improve indexing, search performance, reliability, and scalability while implementing monitoring, automation, and CI/CD. Troubleshoot production issues, contribute to architecture and code reviews, mentor engineers, and collaborate with platform and product teams.

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.

Build and optimize HubSpot environments across multiple clients, including CRM architecture, data governance, workflows, automations, integrations, APIs, webhooks, migrations, reporting data, and troubleshooting. Audit data quality, resolve integrity issues, establish scalable standards, document systems, and improve CRM reliability and performance within an agency or client-services environment.

Build scalable, governed analytical data products, semantic layers, reusable data models, certified datasets, and AI-ready analytics foundations. Perform validation, quality checks, optimization, documentation, and lineage management. Collaborate with Analytics, Product, Engineering, Data Engineering, and business stakeholders on project planning and metric governance. Improve self-service reporting, dashboard performance, automation, and data consistency while mentoring junior team members.

Leads architecture, development, modernization, and integration of enterprise finance technology platforms. Designs scalable data pipelines using Python, Spark, and Kafka; supports financial transaction processing, accounting engines, regulatory reporting, and enterprise data warehouses. Provides technical leadership, establishes engineering standards, drives cloud, DevOps, CI/CD, security, and resiliency practices, resolves complex production issues, and mentors engineering teams while partnering with finance, accounting, risk, audit, and technology stakeholders.

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.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Staff Data Engineer will set technical direction and build Eve’s scalable data platform. Responsibilities include Snowflake administration, role-based access, governance, ingestion, dbt modeling and orchestration, Terraform infrastructure, observability, schema change detection, reliability practices, and CI standards. The role also mentors engineers and analysts, supports AI-assisted development, and establishes secure, reliable workflows for product, case, customer, and go-to-market data.

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.

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.

Build and maintain Snowflake data models using dbt and orchestration tools, improve data quality and observability, define data contracts, and support a modular healthcare data platform. Collaborate with analysts, data scientists, and software engineers to deliver trusted datasets. Monitor and troubleshoot pipelines, optimize Snowflake performance and costs, participate in design reviews, document systems, and support governance, security, and privacy standards for healthcare data.

Build, test, document, and optimize Snowflake data models using dbt. Improve data quality, observability, lineage, and data contracts while collaborating with analysts, data scientists, and software engineers. Monitor pipelines, troubleshoot incidents, optimize query and storage performance, support client-specific configurations, participate in design reviews, and contribute to scalable data platform patterns. The role also requires clear documentation and awareness of security, governance, privacy, and healthcare data requirements.

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.

Leads enterprise data platform architecture and modernization, including migration from Azure SQL Server to Databricks, Lakehouse design, Kafka streaming, Spark optimization, governance, reliability, and CI/CD. Enables AI and agentic data capabilities through governed Delta tables, feature stores, vector stores, RAG pipelines, and Databricks tools. Defines engineering standards, security, disaster recovery, and observability practices while mentoring engineers and partnering cross-functionally on scalable data solutions.

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.

Builds and maintains scalable data pipelines, ETL/ELT infrastructure, analytics platforms, and data delivery frameworks. Responsibilities include data modeling, SQL optimization, quality assurance, governance, troubleshooting, cloud-based big data processing, and stakeholder support. The role also improves data infrastructure scalability, documents processes, educates users on data literacy, and provides guidance to junior staff. Requires a bachelor’s degree or equivalent experience and at least four years of relevant data engineering or analytics experience.

Develops enterprise data pipelines, streaming ingestion systems, AWS data lakes, Snowflake warehouses, analytics solutions, and MarkLogic integrations. Designs data and software architectures, REST APIs, and JavaScript modules while supporting Agile software development. Requires extensive experience with AWS data services, Python, JavaScript, Java, databases, ETL, web development, data integration, and enterprise information architecture.

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.

Leads federal healthcare consulting engagements by managing client relationships, project teams, deliverables, risks, resources, presentations, and strategic initiatives. Assesses client organizations and healthcare operations, supports contract growth, monitors project status, and delivers work under tight deadlines. The role also requires database engineering expertise, including AWS database services, Java or Python, SQL, database optimization, security, and design.

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 operate production pipelines for legal records from PACER, court systems, opinions, PDFs, scans, XML, and HTML. Develop LLM-assisted extraction workflows, validate data quality, optimize SQL, and support RAG and semantic search products. Manage AWS S3 and RDS systems within security guardrails while collaborating with engineering teams. The role requires strong Python and SQL skills, legal data experience, document extraction expertise, and autonomous problem-solving with large, messy datasets.

Build and operate WISEcode’s food data lakehouse using Python, SQL, DuckLake, S3 Parquet, Postgres, and Prefect. Responsibilities include source ingestion, entity resolution, pipeline consolidation, data enrichment, downstream connectors, data quality reporting, configuration discipline, and architectural documentation. The role requires designing and shipping production lakehouse or warehouse systems, managing scalable API-driven pipelines, and collaborating closely with engineers in a fully remote environment.

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.