Top Senior Data Engineer Jobs in Dallas, TX

Designs and maintains scalable data pipelines, develops dbt transformation workflows, and architects Snowflake data models and warehousing solutions. Integrates multiple data sources, implements quality monitoring, troubleshoots pipeline issues, optimizes SQL and warehouse performance, documents architecture, and collaborates with analysts, data scientists, and stakeholders to deliver reliable analytics infrastructure.

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 optimize large-scale ETL/ELT pipelines and lakehouse architectures on cloud platforms using Databricks, Spark, Delta Lake, and Snowflake. Lead data federation with Starburst, improve performance and governance, support AI/ML projects (RAG/Agentic AI), mentor engineers, manage stakeholders, and deliver solutions in an Agile environment.

Lead design and implementation of scalable cloud data platforms and ETL/ELT pipelines using PySpark/Databricks, Delta Lake, and Snowflake. Optimize Spark workloads, enforce data governance, support AI/ML (including RAG and Agentic AI), manage data federation with Starburst, mentor engineers, and engage stakeholders in an Agile environment to deliver enterprise-scale analytics solutions.

Build, implement, and support enterprise data and ETL systems across the full development lifecycle. Conduct systems analysis, design data warehouse and cloud solutions, provide operational support, manage risks, and implement security controls. Collaborate with business and technology stakeholders, resolve complex technical issues, establish development standards, and mentor junior analysts. The role requires expertise in AbInitio, AWS orchestration tools, multiple data platforms, Python, PySpark, Unix scripting, and banking technology.

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 and build scalable data processing and control-plane solutions on Databricks/AWS. Develop Python/PySpark/SQL pipelines, APIs/SDKs, governance, observability, CI/CD, and operational runbooks. Mentor engineers, drive adoption of AI-assisted development, and ensure data quality, security, lineage, and platform reliability.

Design and build Microsoft Fabric medallion architecture, including Bronze, Silver, and Gold Delta tables, Spark feature pipelines, canonical MERGE workflows, Direct Lake semantic models, and OneLake layouts. Optimize Delta and Parquet physical storage, establish idempotent Python-based writes, size Fabric capacity, and support infrastructure provisioning with Terraform. The role also contributes to risk analytics delivery, performance testing, and potential ClickHouse adoption while partnering with DevOps and technical leadership.

Leads the design, development, and deployment of scalable cloud-native data pipelines for enterprise analytics and reporting. Establishes data engineering standards, governance, security, privacy, and compliance practices. Makes architectural decisions, mentors engineers, conducts reviews, translates business needs into technical solutions, and owns end-to-end data initiatives in an Agile environment.

Design, develop, and maintain enterprise data solutions and pipelines using Azure data platform and Microsoft Fabric. Support data modeling, master data management, medallion architecture, data quality, and integrations to produce trusted, scalable data for analytics and BI.

The Senior Data Quality Specialist will develop data quality rules, implement cloud-based solutions, and analyze data accuracy, leading projects and collaborating with teams.

Design, build, and maintain scalable, high-performance data pipelines, ingestion, transformation, and data export solutions. Implement data quality, testing, and monitoring, collaborate with product and stakeholders to build analytical data models, support production jobs, perform PoCs, and contribute to roadmap, standards, and documentation.

Design, build, and operate scalable batch and streaming data pipelines and analytical data models. Lead data architecture across ingestion, transformation, storage, and serving layers while implementing testing, CI/CD, observability, quality, lineage, governance, and access controls. Integrate restaurant and enterprise data sources, optimize platform performance and cost, deliver production-ready datasets for BI and AI, mentor engineers, and collaborate with cross-functional technical stakeholders.

Design and implement scalable data pipelines, support data ingestion and quality, collaborate on data models, and maintain data exports, contributing to technology improvements.

Hands-on senior data engineer to design, build, and optimize Hadoop-based and real-time streaming data platforms (Kafka/Spark). Lead end-to-end delivery, integrate AI/ML and MLOps, enforce data security/governance in banking, mentor teammates, and improve performance and reliability of data pipelines.

Design, develop, maintain, and optimize enterprise Tableau dashboards and BI solutions. Integrate and cleanse data from Oracle/Sybase/Big Data, tune SQL, automate Tableau extracts, manage Tableau Server on Linux, support testing, UAT, deployments, and troubleshoot data integrity and performance issues.

Design, build, and optimize enterprise-scale data platforms and ETL/ELT pipelines within Azure and Microsoft Fabric. Architect ingestion, transformation, and serving layers, implement Data Vault 2.0 and medallion architectures, enable low-latency distributed processing, ensure data quality for high-volume transactional sources, and collaborate with architects, analysts, and stakeholders to deliver scalable analytics solutions.

Lead design, build, and operate scalable AWS-based data pipelines and real-time ingestion for analytics and ML. Implement data modeling, automated validation, observability, and event-driven architectures; mentor engineers and collaborate with Product and ML teams to productionize feature stores and ML-ready datasets.

Design, build, and operate large-scale batch and real-time data pipelines. Implement ingestion, transformation, orchestration, streaming, and downstream delivery using Kafka, Spark, cloud platforms, and workflow tools. Ensure reliability, scalability, performance, security, secrets management, and observability while collaborating with Agile teams to support enterprise analytics workloads.

Design, build and deliver SAP-originated and non‑SAP data in formats consumable by non‑SAP tools and external B2B partners using SAP BO/Data Services, BW/BI. Support ETL, integration and data delivery processes.