Top Data Engineer Jobs

Own the design, development, maintenance, and performance of Microsoft Fabric data pipelines, centralized semantic models, DAX measures, and data architectures supporting marketing analytics. Ensure data quality, documentation, consistency, and trustworthy business metrics. Partner with data engineering and business intelligence teams on infrastructure and deployments, investigate model issues, perform complex analysis, mentor apprentices, and contribute to applied AI initiatives.

Designs and optimizes scalable Azure Databricks data pipelines, Lakehouse architectures, Delta Lake layers, data models, and distributed Spark processing. Partners with analytics and data science teams to deliver reliable datasets for AI initiatives. Develops autonomous agents using Mosaic AI, LangChain, or AutoGen; builds vector search indexes; and implements retrieval, orchestration, evaluation, monitoring, alerting, observability, and governance capabilities.

Architects and leads enterprise data engineering platforms, including scalable ETL/ELT pipelines, streaming frameworks, cloud infrastructure, data warehouses, and AI/ML data systems. Establishes engineering standards, governance, monitoring, data quality, security, and schema management practices. Designs AI agents using LLMs, partners across business and infrastructure teams, and makes high-impact architectural decisions for reliable, secure, multi-region data environments.

Operate and architect large-scale Apache Kafka and Confluent environments for mission-critical streaming workloads. Responsibilities include cluster sizing, configuration, security, automation, observability, high availability, disaster recovery, and developer enablement. The engineer will collaborate with application and business teams, troubleshoot production issues, document solutions, participate in design and code reviews, and mentor junior engineers. Strong Kafka internals, DevOps, SRE, scripting, and infrastructure-as-code expertise are required.

Develop scalable data pipelines, architectures, models, and integrations for enterprise analytics. Translate business requirements into technical data solutions, implement governance and quality frameworks, optimize data lakes and distributed data stores, automate workflows with modern orchestration tools, and support regulatory compliance and continuous improvement across cross-functional teams.

Designs, builds, and maintains secure data pipelines, ETL processes, data warehouses, and quality controls. Collects, validates, cleanses, and analyzes data; develops SQL queries, Tableau dashboards, and AWS-based solutions; investigates data integrity issues; documents standards; and partners with stakeholders to deliver technical solutions using Agile and DevOps practices.

Designs and builds scalable enterprise data pipelines, integrations, models, warehouses, and cloud data platforms. Translates business needs into data solutions, improves data quality and governance, applies software engineering and AI-assisted development practices, and contributes to architecture, modernization, technical standards, and technology roadmaps. Provides technical leadership to engineers while supporting reporting, analytics, automation, and enterprise decision-making.

Build and optimize data architecture, pipelines, transformation processes, and data delivery systems. Analyze, profile, model, and quality-review large datasets; translate business requirements into data solutions; troubleshoot queries and data issues; and support developers, database architects, analysts, and data scientists. The role also researches data sources, improves data management processes, performs root-cause analysis, and delivers recommendations for data quality and system improvements.

Build and maintain scalable data pipelines, ETL workflows, databases, and cloud-based data storage for digital marketing data. Ensure data quality, accuracy, and performance while preparing datasets for dashboards and reporting. Collaborate with analysts, CSMs, leadership, and cross-functional teams to address data needs, troubleshoot issues, participate in code reviews, and support scalable data management processes.

Designs, develops, and maintains Snowflake data pipelines using Matillion for analytics and business intelligence. Responsibilities include ETL/ELT development, database optimization, data modeling, governance, quality assurance, documentation, troubleshooting, and integration of structured and unstructured data. The role uses Python, SQL, and PowerShell, collaborates with business and technical stakeholders, supports reporting initiatives, and evaluates emerging data technologies.

Designs trusted Snowflake data products and enterprise semantic models that standardize business concepts, metrics, relationships, and rules. Builds Cortex semantic views and agents, validates AI-generated results, develops regression tests, and maintains metadata, lineage, and documentation. Leads modeling initiatives from discovery through production while partnering with business and technical stakeholders to improve analytics, automation, and AI consistency.

Builds and maintains cloud data pipelines, transformations, semantic views, conformed dimensions, and analytical data models. Implements data quality controls, lineage, documentation, testing, release-readiness evidence, and defect resolution. Collaborates with data owners, governance teams, engineers, security stakeholders, data scientists, and customers to deliver reliable, governed data products supporting analytics, reporting, self-service BI, and AI/ML use cases.

Build and maintain data pipelines into Snowflake, support orchestration, testing, and CI/CD, and partner with data scientists on machine learning pipelines. Monitor and troubleshoot pipeline health, improve reliability and performance, and ensure data is accurate, documented, and accessible. The role requires clean, tested, version-controlled code and collaboration with analysts and business teams. This is an entry-level, in-office position in Dallas.

Build and maintain Python jobs that execute SQL against Snowflake to create reliable payments, settlement, fee, and reconciliation datasets. Responsibilities include writing optimized SQL, containerizing scheduled jobs with Docker, implementing data-quality and reconciliation controls, monitoring Snowflake costs, applying payments security controls, and supporting testing, CI, documentation, and code reviews. Collaborate with finance, operations, and product stakeholders while improving development efficiency with AI tools.

Designs and operates scalable GCP data pipelines, reusable engineering frameworks, feature stores, orchestration workflows, and governed data models supporting AI and machine learning. Builds Python, SQL, and Beam transformation libraries; ensures workload cost, performance, reliability, quality, lineage, and observability; and partners with data scientists migrating models to GCP. The role also documents best practices, provides enablement, and supports production systems during off-hours.

Build scalable, secure GCP data pipelines and reusable frameworks supporting machine learning and AI workloads. Develop Python, Go, SQL, and Beam transformation libraries; implement orchestration, data modeling, feature stores, semantic layers, governance, quality, lineage, and observability. Optimize workload performance and costs, migrate models to GCP, and partner with data scientists. The role also includes documentation, workshops, CI/CD integration, and occasional off-hours production support.

Build and maintain Ab Initio data pipelines for enterprise data lakes and warehouses. Perform testing, debugging, data profiling, quality-rule definition, business-term mapping, and data analysis for ingestion and curation. Document data lineage in enterprise metadata repositories and collaborate with product, program, Scrum, data, and business teams in a SAFe Agile environment. The role requires SQL, shell scripting, AWS, database, CI/CD, and scheduling-tool knowledge.

Designs and optimizes scalable ELT/ETL pipelines using dbt, SQL, Snowflake, and Azure. Leads data architecture, dimensional modeling, performance tuning, CI/CD, data quality, observability, lineage, governance, and security. Partners with stakeholders to create reliable analytics assets, evaluates GenAI applications, mentors junior engineers, and supports modern cloud data operations.

Supports data onboarding, pipeline monitoring, troubleshooting, maintenance, validation, metadata management, lineage documentation, and cloud platform operations. Develops Python-based ingestion and automation scripts, assists with AWS deployments and monitoring, participates in incident response, and documents operational procedures. Collaborates with senior engineers while working with Amazon S3, AWS Glue Data Catalog, CloudFormation, Databricks, and related data technologies.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

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 enterprise Master Data Management architecture, implementation, integration, data quality, governance, and operational excellence. Designs entity models, matching and survivorship rules, stewardship workflows, ingestion and synchronization pipelines, APIs, event-driven integrations, and cloud data solutions across Azure, Microsoft Fabric, and Databricks. Provides technical leadership and mentorship, owns the MDM roadmap, establishes DevOps and engineering standards, ensures security and regulatory compliance, and partners with business and technology stakeholders to deliver trusted master data products.

Leads enterprise Master Data Management capabilities, including entity modeling, matching, survivorship, hierarchies, stewardship, governance, data quality, and integration pipelines. Designs cloud-native, API-driven, event-based solutions across Azure, Microsoft Fabric, Databricks, operational systems, and analytics platforms. Establishes engineering and DevOps standards, mentors technical teams, ensures security and compliance, and partners with business and technology stakeholders to deliver trusted master data products.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Develop and manage data integration solutions for Nationwide Financial products using a Snowflake-based data lakehouse. Acquire, curate, and publish data for analytical and operational use cases while supporting secure software delivery, DevSecOps, testing, documentation, deployment, and continuous improvement. The role collaborates across teams, manages project deliverables, and provides technical guidance in an agile, hybrid work environment.