Top Senior Data Engineer Jobs in Dallas, TX

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.

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

Design, build, and maintain Microsoft Fabric pipelines and lakehouse models; optimize Power BI semantic models; define ingestion frameworks and engineering standards; enforce SDLC (Git/CI-CD/testing); mentor teammates; ensure SLA reliability, scalability, and trusted data for client-facing analytics.

Design, build, and maintain large-scale data infrastructure and pipelines (data lakes/warehouses). Integrate diverse data sources, optimize processing and queries, enforce data quality and security, implement monitoring/alerting, troubleshoot issues, and collaborate with cross-functional teams while exploring new data frameworks and cloud services.

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, implement, and maintain PySpark-based data reconciliation solutions for financial systems. Build matching algorithms, integrate with rules engines, process large distributed datasets, identify and resolve discrepancies, and collaborate with analysts and architects to improve data quality and governance.

Design, implement, and maintain PySpark applications to automate large-scale financial data reconciliations. Build transformation and matching algorithms, integrate with rules engines, analyze data gaps, and collaborate with analysts and architects to ensure data quality and system resilience.

Design, build, and optimize PySpark applications and ETL pipelines to process large-scale datasets from SQL/NoSQL sources, data lakes, and streaming platforms. Ensure data quality, error handling, performance tuning, and collaborate with analysts, scientists, and architects to deliver scalable data solutions.

Design, develop, and maintain PySpark applications and ETL pipelines to process, transform, and integrate large-scale datasets from SQL, NoSQL, data lakes, and streaming sources. Optimize Spark job performance, implement robust error handling, and collaborate with data analysts, scientists, and architects using orchestration tools like Airflow or Luigi.

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 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.

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.

The role requires designing, building, and maintaining data pipelines using Big Data technologies. Responsibilities include developing ETL processes, ensuring data integrity, collaborating with teams, and applying AI techniques to drive efficiency.

The Data Engineer will design and maintain data pipelines using Big Data technologies, optimize ETL processes, and apply AI techniques. Responsibilities include collaborating on data solutions and ensuring data quality and security.

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.

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.

Design and build scalable cloud data pipelines and lakehouse architectures for high-volume manufacturing and IoT data. Bridge OT/IT using industrial protocols, apply OEE/Six Sigma/SPC practices to improve yield and uptime, and use Spark, Kafka, Airflow, Delta Lake on Databricks/Snowflake/AWS/Azure. Work closely with shop-floor systems (MES/SCADA/ERP) to enable real-time data extraction and analytics.

The Data Engineer will optimize data infrastructure, modernize SQL processes, support data governance, and mentor team members while enhancing analytics capabilities.