Top Data Engineer Jobs

Build and operate scalable paid media data pipelines that ingest, model, monitor, and reconcile advertising spend, performance, and attribution data. Integrate with advertising platforms, support campaign reporting and dashboards, maintain data quality and observability, and develop distributed data systems and marketing utilities. Translate marketing requirements into technical solutions, contribute to architecture, document decisions, lead complex projects, review code, and mentor junior engineers.

Build and operate paid media data pipelines that ingest, model, monitor, and reconcile advertising spend, performance, attribution, and web analytics data. Develop scalable distributed systems and marketing utilities using AWS and modern data technologies. Integrate advertising APIs, support dashboards and reporting, ensure data quality, and adapt pipelines as platforms evolve. Partner with marketers, analysts, and leadership to define solutions, while mentoring engineers, leading code reviews, and managing complex projects.

Develops and supports scalable enterprise data services and platforms. Responsibilities include building ETL/ELT pipelines, modernizing Hadoop systems to cloud-native architectures, monitoring platform health, optimizing queries and costs, creating observability dashboards, and supporting data migrations. Partners with business stakeholders to deliver analytical solutions and applies AI and Generative AI tools to improve automation, governance, and platform efficiency.

Processes structured and unstructured data, builds scalable data solutions, validates data quality, supports enterprise data warehouses and analytics tools, and maintains deployed data products. Collaborates with analytics teams during Agile planning, improves processes and interfaces, supports data feeds and mappings, and monitors emerging data technologies. Requires experience with Snowflake, relational databases, ETL, data quality tools, business intelligence platforms, Git, and scalable software systems.

Supports the Mobile Device Data and Operations team by collecting, enriching, and distributing product data to catalogs and vendor partners. Develops workflow designs using KNIME or Alteryx, works with Oracle and Snowflake databases, and deploys application tools on AWS. The internship also includes professional development, mentorship, networking, community service, and cross-functional projects.

Designs, builds, and operates scalable data pipelines, services, and products supporting clickstream and behavioral data. Responsibilities include data modeling, API and system design, automated quality frameworks, observability, governance, reliability, security, and performance optimization. The role collaborates with engineering, product, analytics, and domain teams, translates complex requirements into production solutions, and applies AI/ML-enabled tools and workflows. It also contributes to technical decisions, data-driven improvements, and operational excellence.

Designs, builds, and operates scalable cloud data platforms, APIs, pipelines, and data products. Leads architectural decisions, evaluates platforms such as Snowflake and Databricks, develops distributed and streaming workloads, enforces engineering standards, and mentors data engineers. Collaborates with product managers, software engineers, analysts, and data scientists to deliver resilient solutions. Requires extensive experience with application development, distributed data, SQL, programming, data pipelines, data modeling, cloud platforms, and relational and NoSQL databases.

Design, build, and lead scalable cloud data pipelines, platforms, and end-to-end data solutions. The role involves distributed data engineering, data modeling, relational and NoSQL databases, real-time processing, warehousing, architecture decisions, and evaluating platforms such as Snowflake and Databricks. Responsibilities also include mentoring engineers, enforcing engineering standards, collaborating across Agile teams, and delivering transformative data initiatives independently.

Designs, builds, and supports scalable data warehouse solutions, including pipelines, transformations, data models, governance practices, metadata, and documentation. Develops source-to-target mappings, DDL documentation, and SCD models; troubleshoots data issues and performs root cause analysis. Collaborates with engineering, quality assurance, reporting, and business teams on data migrations, modernization, releases, and code reviews. Leads data requirements analysis and data solution development across multiple platforms.

Leads the design, development, and maintenance of scalable data warehouse solutions, including pipelines, data models, transformations, mappings, and documentation. Supports data governance, metadata management, migration, modernization, troubleshooting, and root cause analysis. Collaborates with engineering, quality, business analysis, and reporting teams to deliver reliable data solutions across multiple platforms and applications.

Designs, builds, and maintains data warehouse solutions, pipelines, models, transformations, and data services across multiple platforms. Leads data requirements analysis, governance, metadata management, source-to-target mapping, migration, modernization, troubleshooting, and root-cause analysis. Collaborates with engineering, business, quality, and reporting teams to deliver scalable, accurate, secure, and maintainable data solutions supporting asset management operations.

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.

Provides data architecture leadership for Payments Operations, defining data models, metadata, authoritative sources, quality rules, and data flows. Designs and implements analytics and data delivery capabilities, profiles and transforms data using Python, PySpark, and SQL, and evaluates data fitness for business processes. Partners with technology, product, analytics, and data teams to develop solutions, apply governance standards, and research emerging technologies.

Designs, develops, tests, and maintains data architectures, ETL pipelines, databases, and large-scale processing systems. Builds scalable data infrastructure, improves data delivery processes, and ensures data quality, integrity, availability, and performance. Collaborates with data scientists and architects while troubleshooting pipelines and systems. The role requires a technical bachelor’s degree, at least two years of data engineering experience, U.S. citizenship, and knowledge of SQL, data warehousing, and data integration.

Designs and maintains reusable data ingestion and transformation components, automated quality and governance checks, reliable pipelines, monitoring, deployment automation, cloud integrations, containers, and infrastructure as code. Partners with data consumers on usable models and datasets, documents standards and runbooks, supports Agile improvement, and responds to critical IT incidents. Requires 5+ years of data engineering experience, orchestration, distributed processing, storage, scripting, automation, and version control expertise.

Designs and implements reusable data ingestion and transformation components, automated quality and governance checks, reliable pipelines, monitoring, alerting, and observability practices. Supports CI/CD, cloud integrations, container orchestration, infrastructure as code, deployment automation, data modeling, documentation, and Agile platform operations. The role also contributes to incident response and critical escalations.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while applying distributed data, lakehouse, streaming, and full-stack technologies. Develop secure, resilient, and reusable data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Mentor technical contributors, enforce engineering patterns, communicate outcomes, and support data privacy and access controls.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed and streaming data workloads, data models, and warehousing solutions using Python, Spark, Databricks, Snowflake, and relational and NoSQL databases. Establish reusable engineering patterns, security controls, and operational standards while collaborating with product and engineering teams. Mentor data professionals, communicate technical concepts to stakeholders, and deliver resilient data solutions for enterprise use.

Designs and leads scalable Azure data platforms, ETL/ELT pipelines, Spark workflows, AI integrations, and cloud applications. Responsibilities include data modeling, advanced SQL optimization, reliability improvements, API integration, AI-powered automation, architectural standards, stakeholder analysis, mentoring, and evaluating emerging technologies. The role uses Azure Databricks, Snowflake, Spark, Python, and modern software engineering practices to support analytics, machine learning, business intelligence, and healthcare data solutions.

Leads enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, AI, and vector-store use cases. Establishes data quality, governance, lineage, observability, access-control, and reliability standards. Defines AI-enabled data engineering patterns, mentors engineers, resolves complex architecture issues, and influences cross-functional roadmaps. Requires hands-on technical leadership and 15+ years of software, data engineering, or architecture experience.

Designs and manages enterprise finance data architectures, pipelines, transformations, semantic layers, retrieval indexes, and reusable data products. Partners with AI engineers on RAG, embeddings, vector databases, and data refresh processes. Evaluates data quality, lineage, security, privacy, and governance while developing synthetic or de-identified datasets. Implements monitoring, validation, exception handling, and experimentation datasets, and helps guide emerging data and AI architecture decisions.

Develop and maintain scalable data pipelines and storage solutions for healthcare data. Support Kafka real-time ingestion, Spark batch processing, AWS Athena analysis, reporting, data quality monitoring, troubleshooting, documentation, governance, and security compliance. Collaborate with data scientists, analysts, engineers, and stakeholders while using approved AI and generative AI tools to improve workflows and automate tasks.

Develops, tests, deploys, and maintains business-critical ETL/ELT pipelines and data architectures. Responsibilities include SQL and Oracle development, data modeling, performance tuning, semi-structured data processing, pipeline testing, control validation, and secure handling of enterprise data. The role also integrates Databricks and ThoughtSpot, validates AI-assisted outputs, supports data governance, and collaborates across ingestion, transformation, and serving layers.

Designs, builds, and optimizes scalable data pipelines, real-time databases, and data warehouse solutions. Maintains data quality, reliability, accessibility, and processing performance while supporting data platform operations. Collaborates with stakeholders to translate data requirements into business solutions, documents data architecture, implements ETL processes using microservices, and ensures compliance with company policies and industry standards.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Responsibilities include data modeling, architecture, distributed processing, relational and NoSQL databases, data security, observability, and real-time workloads. The role collaborates with product and engineering teams, establishes reusable engineering patterns, communicates technical concepts, and mentors data professionals while delivering resilient solutions.