Top Senior Data Engineer Jobs in Dallas, TX

Lead design and implementation of data infrastructure, pipelines, and warehouses using Azure Data Factory, Databricks, and Snowflake. Manage data modeling, quality, security, and analytics frameworks. Lead client accounts, mentor teams, and drive innovation to deliver data-driven business insights.

Lead design and implementation of scalable data infrastructure, pipelines, and transformations using cloud platforms. Build and optimize data warehouses (Snowflake, Databricks), orchestrate workflows (Airflow, Hadoop), ensure data quality and security, and coach teams to deliver analytics solutions that drive tax practice insights and client outcomes.

Design and build data infrastructure, pipelines, and transformations using Azure Data Factory, Azure Databricks, and Snowflake. Develop data architecture and modeling, maintain data lakes/warehouses, ensure data quality and security, optimize performance, and collaborate with clients to deliver tailored data solutions and governance.

Design, implement, and manage advanced data ecosystems and scalable ETL/ELT pipelines. Analyze complex problems, deliver data warehousing and governance solutions, mentor junior staff, and build client relationships while maintaining high standards and business-context awareness.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Lead design and deliver enterprise data governance: implement data models, metadata standards, data cataloging/lineage, governed ETL/ELT pipelines, data quality controls, and coach teams on AI-assisted engineering and secure practices.

Builds and maintains scalable ETL processes, database systems, data products, and real-time or offline analytics tools. Integrates large-scale data, ensures pipeline reliability and data integrity, translates business requirements into maintainable code, and supports analytical solutions such as Tableau dashboards. Partners with stakeholders, troubleshoots production issues, conducts testing and releases, provides data guidance, and trains users on tools and data sources.

Designs, develops, and optimizes scalable Azure-based data engineering solutions, including lakehouses, warehouses, ETL/ELT pipelines, Databricks notebooks, and analytics workflows. The role supports enterprise reporting, AI and machine learning initiatives, feature engineering, Generative AI, and MLOps. Responsibilities include performance tuning, CI/CD automation, production support, technical design, lifecycle management, stakeholder collaboration, and translating business requirements into secure, reliable data platforms.

Lead the design and optimization of enterprise data platforms, integrating healthcare and other business systems. Develop high-performance SQL Server solutions, tune T-SQL and ETL workloads, build Azure Data Factory pipelines, and apply Python and AI/LLMs for data extraction. Responsibilities include data modeling, profiling, cleansing, lineage, master data management, monitoring, automation, stakeholder collaboration, mentoring junior engineers, and establishing data engineering best practices.

The Lead Data Engineer will design, develop, and support complex technical solutions using Java, Python, AWS, and big data technologies. Responsibilities include collaborating with Agile teams, mentoring, and delivering cloud-based solutions.

Lead design and implementation of large-scale data pipelines, ETL, and warehousing. Build and manage databases, ensure data quality/security, collaborate with analysts and scientists, evaluate new technologies, mentor junior engineers, and manage project delivery.

Design and maintain scalable cloud data pipelines, ELT/ETL workflows, data models, ingestion frameworks, and automation systems supporting cloud cost visibility and optimization. Optimize performance, reliability, and cost across AWS, Snowflake, and related platforms. Ensure data quality, validation, auditability, and production troubleshooting while partnering with Engineering and Finance stakeholders on usage-based insights and cost-aware decisions.

Design and maintain scalable cloud data pipelines, ELT/ETL workflows, data models, and automation systems supporting cloud cost visibility and optimization. Use Python, SQL, dbt, Airflow, AWS services, and Snowflake to process telemetry and usage data. Optimize performance, reliability, storage, and compute costs while ensuring data quality, auditability, and validation. Partner with Engineering and Finance stakeholders on cost-aware decision-making and usage insights.

Designs, builds, and operates scalable enterprise data platforms supporting ingestion, processing, storage, governance, analytics, and AI/ML. Responsibilities include developing batch and real-time pipelines, lakehouse and warehouse architectures, orchestration, infrastructure automation, CI/CD, monitoring, security, cost optimization, and observability. The role partners with data, ML, DevOps, security, and application teams, troubleshoots complex platform issues, establishes reusable engineering standards, contributes to architecture strategy, and mentors engineers.

Designs and delivers scalable, low-latency streaming data pipelines using Apache Flink, Confluent Kafka, and Apache Iceberg. Modernizes batch and RDBMS workflows into event-driven architectures, builds lakehouse data structures, ensures data quality, optimizes streaming performance, and integrates ITSM and enterprise systems. The role also leads testing, documentation, delivery lifecycle activities, stakeholder collaboration, CI/CD, operational monitoring, and mentoring of data engineers.

Build and scale data infrastructure supporting utility billing reports, analytics, AI-ready retrieval, dimensional models, and customer-facing data products. Responsibilities include developing ingestion and transformation pipelines, creating facts and dimensions, implementing testing and observability, monitoring freshness and lineage, and partnering with engineering, product, compliance, and analytics teams.

Provides technical leadership for enterprise data platform engineering. Designs and implements ETL, ELT, streaming ingestion, data models, cloud data lakes and warehouses, monitoring, data quality, governance, testing, version control, and CI/CD practices. Leads AWS, Snowflake, and dbt implementations while integrating disparate data sources for analysts and data scientists. Partners with business stakeholders to align data products with organizational strategy and enables self-service analytics and automation.

Build and scale a client-facing analytics platform using Microsoft Fabric, scalable pipelines, Lakehouse architectures, dimensional models, and Power BI semantic models. Establish ingestion, observability, testing, CI/CD, and SDLC standards while optimizing performance, refresh strategies, costs, and SLAs. Lead architecture decisions, communicate technical concepts to business stakeholders, mentor engineers, and proactively manage platform risks in a client-facing analytics environment.

Design, build, optimize, and maintain scalable data pipelines and platform components supporting analytics and AI/ML. Own complex engineering initiatives, resolve performance and production issues, improve governance and data models, and contribute to infrastructure as code and CI/CD. Provide technical leadership through architecture guidance, code reviews, mentoring, documentation, and cross-functional collaboration. Support platform modernization across cloud data platforms, warehouses, lakehouses, and streaming or feature-store use cases.

Lead Data Engineer/DB2 DBA responsible for maintaining and optimizing mainframe Db2 databases and data pipelines. Tasks include troubleshooting DB2 issues, backup/recovery, DDL deployments, environment refreshes, performance tuning, control reviews, and collaborating with application teams. Uses enterprise-authorized AI to accelerate design and operational tasks while ensuring validation, security, and auditability.

Lead and mentor a data engineering team to design, develop, test, and support cloud-based big data and streaming solutions. Build full-stack data pipelines using Java/Scala/Python, RDBMS/NoSQL, Redshift/Snowflake, and cloud platforms. Collaborate with product managers, perform unit tests and code reviews, and promote engineering best practices and tech community engagement.

Build, test, and operationalize complex data pipelines and solutions on cloud platforms. Analyze and transform data, ensure data governance, security, and quality, troubleshoot defects, and collaborate across teams to support analytics, ML/AI, and business intelligence needs.

The Senior Lead Data Engineer designs and develops technical solutions, leads a team of developers, and utilizes big data and cloud technologies to deliver robust solutions for Capital One.

Lead and mentor data engineering teams to design, build, test, and support cloud-based, full-stack data solutions. Work with big data, streaming, and data warehousing technologies (Redshift/Snowflake), collaborate with product managers, and ensure high-quality, performant code and systems.

Senior Data Engineer responsible for designing, building, and optimizing enterprise data pipelines, architectures, and scalable reporting solutions. The role integrates data from diverse business platforms, develops Snowflake and dbt solutions, implements governance and security, and enables self-service analytics. It partners with executives, product teams, analysts, developers, and data scientists while mentoring team members, evaluating emerging technologies, and delivering reliable data assets and actionable insights.