Top Data Engineer Jobs

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.

Designs and maintains data collection infrastructure, resilient pipelines, ETL/ELT processes, data warehouses, streaming architecture, BI reporting, and analytical systems. Troubleshoots technical defects, performs root-cause analysis, maintains specialized datasets, and owns end-to-end data collection, extraction, and cleansing. The role requires experience with AWS, Google Cloud, ETL tools, programming languages, NoSQL databases, and BI platforms.

Build and maintain batch data pipelines and ETL/ELT workflows using SQL, Python, APIs, databases, and files. Develop datasets, data models, quality checks, monitoring, documentation, tests, and deployment processes. Troubleshoot pipeline issues, support releases and backfills, modernize legacy processes toward secure cloud-based solutions, and provide business intelligence insights while collaborating with analysts, software engineers, and business partners.

Lead architecture, development, and optimization of a cloud-native data platform. Design high-volume batch and streaming pipelines, optimize data processing across AWS, Snowflake, Redshift, and Athena, and implement Iceberg-based data management. Establish security, privacy, observability, and DataOps practices while mentoring engineers, guiding technical direction, collaborating with data science teams, and delivering scalable petabyte-level data infrastructure.

Lead the architecture, development, and modernization of Prodege’s large-scale data platform. Build batch, ELT, streaming, and near-real-time pipelines supporting BI, experimentation, product analytics, machine learning, and AI applications. Establish data modeling, governance, observability, schema management, and data contract standards across Snowflake, dbt, Iceberg, Trino, and Kafka. Drive technical implementations, optimize performance and cost, mentor engineers, and partner with cross-functional teams to deliver reliable, scalable data systems.

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.

Develops and implements data engineering and statistical analysis solutions for government customers. Responsibilities include automating data extraction and processing, building data models, analyzing large and diverse datasets, developing statistical methods, creating databases, reports, and visualizations, and communicating technical findings. The role requires independent problem-solving, data science and engineering experience, and support for intelligence-related data capabilities.