Top Senior Data Engineer Jobs in Dallas, TX

Develop and implement data solutions, pipelines, architectures, analytics, reports, and dashboards for clients. Apply Python, Java, ETL, business intelligence, and advanced analytics techniques to interpret complex data, improve operations, and support strategic decisions. Collaborate with stakeholders, manage client relationships, identify data challenges, and deliver high-quality managed services work while contributing to continuous process improvement.

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.

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.

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.

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 data engineering initiatives by designing scalable data pipelines, services, architectures, models, and structures using Hadoop, Ab Initio, Exadata, SQL, and cloud platforms. Analyze and troubleshoot complex data in warehouse environments, support risk management and business intelligence, resolve production and data integrity issues, gather requirements, lead designs, and mentor engineering staff throughout the project lifecycle.

Design, build, and maintain optimized, highly available data pipelines and the BMG data warehouse. Lead medium-to-complex data engineering initiatives, perform solution design, code reviews, QA, data integration, and collaborate across teams. Support cloud migration and ensure compliance with data policies for reporting and analytics.

As a Lead Data Engineer, you'll lead the design and implementation of scalable data platforms, build distributed streaming data pipelines, and collaborate on technical solutions using big data technologies and agile methodologies.

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.

Designs, builds, and optimizes SAP HANA-native data models and analytical objects for S/4HANA, BW/4HANA, and standalone HANA platforms. Translates business requirements into performant models using calculation views, CDS views, AMDPs, and SQLScript. Diagnoses and resolves HANA performance issues, applies tuning methodologies, supports replication and security models, and contributes to analytics modeling, migrations, and upgrades.

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.

Leads the design and development of scalable, fault-tolerant streaming data platforms, distributed data pipelines, analytics data stores, and cloud-based solutions. Mentors engineers, establishes data architecture and pipeline best practices, collaborates across Agile teams, and works with security and product partners. The role requires building, testing, tuning, and supporting real-time data systems using technologies including Flink, Spark, Kafka, Snowflake, Redshift, and cloud services.

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.

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.

Lead deployment and administration of the electronic Quality Management System (eQMS), digitize quality workflows, and build analytics and BI solutions (Power BI) to monitor quality KPIs. Partner cross-functionally to implement automation, support UAT/validation/training, maintain data integrity and governance, and enable future predictive quality and AI-enabled decision support.

Design, build, modernize, and maintain operational and analytical data capabilities for Wealthscape Reporting, Analytics and Insights. Perform solution design, data analysis, ETL development, and production rollouts using Snowflake, AWS, cloud databases, and CI/CD pipelines. Collaborate across teams in a fast-paced financial services environment.

As a Sr Data Engineer, you'll conduct feasibility analyses, develop applications, monitor processes, consult with users, and ensure compliance while serving as a mentor to junior analysts.

Design, build, test, and operationalize complex data pipelines and multi-layer data architectures using Snowflake and dbt. Analyze source systems, perform data mapping and transformations, ensure data governance, quality, and security, resolve defects, optimize query and pipeline performance, and collaborate with engineering and business teams to support BI and AI/ML use cases.

Design, build, and maintain scalable cloud-native data pipelines, microservices, and platforms to support real-time and batch processing. Collaborate with architects and stakeholders to translate requirements, implement AWS-based ingestion/streaming solutions, enforce data quality/testing, and operate observability and CI/CD practices to ensure reliable production data systems for autonomy initiatives.

Build and maintain centralized pricing data infrastructure, scalable workflows, and data pipelines; integrate AI capabilities; deliver pricing tools, reporting, and governance to support data-driven pricing and budgeting across the firm.

Design, build, and maintain enterprise analytics solutions and semantic data models using Microsoft Fabric, Databricks, SQL, Python, and BI tools. Deliver scalable reporting datasets, executive dashboards, data quality standards, and CI/CD practices. Lead architecture discussions, mentor engineers, collaborate with stakeholders, and drive analytics strategy to enable data-driven decisions.

Lead design, build, and operate scalable data pipelines, microservices, and cloud-native data platforms. Collaborate with architects and stakeholders to translate requirements, ensure data quality and testing, implement streaming and batch solutions on AWS, and maintain operational excellence, monitoring, and CI/CD for high-availability data systems supporting autonomy initiatives.