Top Data Engineer Jobs

Lead the architecture, development, and operation of scalable data platforms. Design ETL workflows, pipelines, data models, APIs, and distributed systems using Spark, Python, and SQL. Drive production deployments, infrastructure automation, observability, security, disaster recovery, and incident resolution. Make cross-team architecture decisions, apply AI-assisted engineering responsibly, improve testing and operational readiness, and mentor engineers while maintaining high standards for scalability, reliability, privacy, and cost.

Develops data algorithms and systems, analyzes structured and unstructured sources, maintains data management tools, and applies statistical, qualitative, and quantitative methods to deliver actionable insights. Collaborates with clients in data-rich environments to interpret information and support informed decisions. The role requires R or Python, data cleansing and parsing, scientific analysis methods, and an active TS/SCI clearance with CI polygraph.

Designs, implements, and operates data management systems supporting defense intelligence requirements. Responsibilities include developing data architectures, optimizing data throughput and query performance, selecting backend database technologies, and integrating data across systems. The role also requires administering Linux and Windows server environments, VMware or Citix infrastructure, storage, backups, security baselines, LDAP accounts, and PKI authentication. An active Top Secret/SCI clearance with polygraph is required.

Build and maintain data infrastructure, dbt models, SQL transformations, and analytical data models for advertising measurement and attribution. Integrate and reconcile large event, impression, media, and conversion datasets across multiple sources. Investigate data-quality issues, define business rules, support reporting and media optimization, and communicate technical decisions to cross-functional teams. Use Snowflake, Python, Git, testing practices, and AI-assisted development tools to create reliable, documented data products.

Build and maintain scalable data platforms, pipelines, and full-stack data applications for mission-driven clients. Develop backend data engineering solutions and interactive analytics front ends using tools such as Python, PySpark, React, Tableau, and Foundry. Collaborate with multidisciplinary teams and customers, translate complex requirements into technical solutions, analyze disparate datasets, and deliver production-quality systems in agile and classified environments.

Leads enterprise data engineering and architecture initiatives, including cloud cost optimization, data governance, security, streaming, ETL, pipeline orchestration, and visualization. Guides data strategy, vendor relationships, escalations, architecture reviews, stakeholder collaboration, and team mentorship. Requires advanced SQL, data streaming, big data, scalable architecture, security, and visualization expertise, with five or more years of experience.

Design, build, and maintain scalable data infrastructure, including ETL/ELT pipelines, data warehouses, data lakes, and Microsoft Fabric lakehouse solutions. Develop SQL, Python, and Spark transformations; implement governance, lineage, quality monitoring, and testing; and support CI/CD and infrastructure-as-code deployments. Collaborate with analysts, data scientists, architects, developers, and business stakeholders to define data requirements, document technical solutions, and deliver reliable enterprise data platforms.

Design and implement data infrastructure, custom extractors, ETL pipelines, data models, and visualization prototypes for industrial clients. Collaborate with solution architects, subject matter experts, product teams, and stakeholders to deliver reliable data solutions using Python, Spark, SQL, Kafka, REST APIs, and Docker. The role requires experience in oil and gas or manufacturing, client-facing communication, and onsite travel from a hybrid Houston location.

Develop and implement client data systems, including database environments, data migration processes, and secure, efficient data access solutions. Contribute to data-system planning for stakeholder approval and collaborate directly with clients on projects. The role requires travel and/or relocation to unanticipated U.S. locations based on client and project needs. Technologies include Talend Enterprise Edition, ETL, Java, SQL, and PL/SQL.

Develop and maintain data lake and data mart architecture, translate client requirements into migration plans, extract and migrate data while preserving integrity and security, and create data visualizations. Collaborate with QA teams to resolve defects and validate systems. The role requires working with ETL, SQL, Oracle, Informatica, Teradata, and Power BI, plus travel or relocation to client project locations across the United States.

Builds and maintains ETL and ELT data pipelines and data models, incorporating data quality controls and performance optimization.

Design, develop, and deploy end-to-end real-time streaming and ETL/ELT solutions on AWS (including geospatial pipelines). Build APIs, implement infrastructure-as-code, ensure security/compliance, and move systems to production and O&M for high‑scale distributed environments.

Architect, deploy, secure, automate, and operate large-scale Apache Kafka and Confluent environments for mission-critical streaming workloads. Responsibilities include cluster sizing, configuration, observability, high availability and disaster recovery, developer enablement, troubleshooting, infrastructure as code, and collaboration with application teams. The role also involves design and code reviews, documentation, communication, and mentoring junior engineers.

Data Engineer supporting a United States Air Force program. Responsibilities include designing ETL pipelines, data warehouses and lakes, APIs, data quality controls, metadata and lineage practices, analytical datasets, security controls, automated deployments, monitoring, troubleshooting, and technical documentation. The role collaborates with government stakeholders, mission partners, technical teams, and data scientists while maintaining compliance with approved architectures, governance standards, and security boundaries.

Develop and maintain logical and physical data models, architectures, schemas, pipelines, and ETL processes. Ensure data governance, compliance, integration, reuse, and quality. Analyze data requirements, create decision-support materials, and brief senior leaders. The role requires extensive experience in data engineering, ORSA, data science, warehousing, ingestion, profiling, SQL, pandas, Python, and distributed data environments, along with an active TS/SCI clearance.

Build and optimize scalable batch and streaming data pipelines for Comcast’s enterprise Data Lake. Develop cloud-native solutions using AWS, Databricks, PySpark, Kafka, Kubernetes, and Airflow/MWAA. Support high-volume distributed data processing, production troubleshooting, data warehousing, automation, observability, and data quality initiatives. Collaborate with product, governance, analytics, and engineering teams, participate in architecture decisions, mentor junior engineers, and improve platform reliability and performance.

Lead administration and strategic evolution of a petabyte-scale Snowflake data warehouse supporting more than 900 users. Responsibilities include platform reliability, incident response, security and governance, monitoring, automation, schema migration, cost optimization, data quality, and compliance. The role partners with data and analytics teams, evaluates technologies, leads architecture reviews, and mentors engineers on Snowflake administration and data warehouse best practices.

Build and maintain scalable batch, streaming, and real-time data pipelines, platforms, data lakes, warehouses, and lakehouses. Implement data quality, governance, security, observability, automation, and cloud modernization. Partner with product, engineering, analytics, data science, and business teams to deliver analytics, AI/ML, reporting, and customer insight solutions. Troubleshoot production issues, guide architecture, mentor engineers, and provide technical leadership across marketing data initiatives.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Supports the Global Technology & Solutions team as a Data Engineer Intern. Assists with departmental projects, reports, operational and administrative tasks, and cross-functional initiatives under supervision. Builds understanding of supported businesses and customers while developing skills in data engineering, data warehousing, and big data. The role is in person in Mount Laurel, New Jersey, up to four days per week, with training and onboarding provided.

Build and operate data platform services, APIs, SDKs, schemas, validation tools, event-driven pipelines, and reverse ETL workflows. Improve Kafka-based data delivery, schema evolution, observability, lineage, access controls, privacy, and compliance. Partner with engineering, analytics, data science, security, and infrastructure teams to create reliable, self-service data workflows. Operate AWS services, infrastructure as code, monitoring, alerting, and incident response.

Lead data engineering initiatives for Wells Fargo’s counterparty credit risk technology team. Design, code, test, debug, document, and deploy scalable data solutions and pipelines using Python, SQL, Power BI, and related technologies. Collaborate with risk and capital markets stakeholders, resolve technical challenges, support reporting and visualization, lead projects, and mentor less experienced staff. The role requires onsite work in Charlotte or Iselin under a hybrid schedule.

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, development, and operation of scalable enterprise data products using Databricks and modern data engineering practices. Provides hands-on technical leadership across architecture, implementation, testing, troubleshooting, production readiness, observability, governance, and performance. Partners with architects and product teams to deliver analytical, operational, reporting, and AI-driven solutions. Establishes engineering standards, promotes responsible AI-assisted development, mentors engineers, and improves platform reuse and productivity.

Designs, builds, and maintains complex data systems, including scalable data infrastructure, batch and streaming pipelines, data models, data products, and cloud-based architectures. Develops automated deployment pipelines, improves data systems, and supports data analysis and advanced analytics. Collaborates with cross-functional teams to gather requirements and ensure robust, governed, and accessible data solutions.