Top Data Engineer Jobs in Dallas, TX

Design, build, test, and maintain secure, scalable data pipelines and architectures. Work with Python, Databricks, SQL/NoSQL, and enterprise AI capabilities to deliver trusted data collection, storage, access, and analytics. Validate AI-assisted outputs, update data models, customize tools, and ensure controls, traceability, and data sensitivity handling.

Build and maintain analytics data models, warehouse tables, views, and automated pipelines using dbt and Snowflake. Ensure data quality, consolidate business-unit data, develop ThoughtSpot dashboards, and analyze complex datasets to support product and operational decisions. Partner with product, engineering, and operations stakeholders, define metrics, improve self-service analytics, leverage AI tools for automation, and document data processes.

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, develop, test, and maintain scalable data pipelines and architectures. Ensure data quality, lineage, governance, and security. Build automated testing/monitoring, optimize storage and query performance, update data models, and support data integration using SQL/NoSQL, cloud, and distributed processing frameworks.

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 the design and development of scalable, fault-tolerant streaming data platforms, distributed data pipelines, analytics data stores, and cloud-based solutions. Lead engineers, establish data architecture and pipeline best practices, develop and review high-performance code, and collaborate across Agile, product, security, and engineering teams. Mentor developers, support machine learning and microservices initiatives, and apply emerging technologies to deliver reliable data solutions.

Design and build analytics-ready data models and a governed semantic layer in Databricks/Snowflake. Translate stakeholder requirements into reusable datasets, implement data quality controls, optimize model performance, document lineage and metrics, and enable self-service BI.

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.

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.

Design, build, and optimize relational database solutions and cloud-based data pipelines using SQL Server/Azure SQL, Databricks/Spark, Python/PySpark. Improve performance, support ETL/ELT, dimensional modeling, and AI/ML data needs. Collaborate with cross-functional teams, troubleshoot production issues, and adhere to governance and security standards.

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.

Design, develop, and support Denodo virtual views and data services; integrate Denodo with databases, APIs and cloud platforms; build reusable semantic/logical layers; optimize queries and performance; collaborate with architects and business teams; document designs, test, review code, deploy, and provide production support.

Migrate data products from Azure Synapse to Microsoft Fabric and design scalable data pipelines using Azure Data Factory, notebooks, Spark/PySpark, Python, and SQL. Implement ingestion (full, delta, CDC), refactor pandas to PySpark, apply medallion architecture, perform data quality checks, tuning, monitoring, troubleshooting, and collaborate with stakeholders to document and deploy solutions.

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 and maintain batch and streaming data pipelines supporting enterprise analytics and AI. Develop transformations, analytical data models, quality checks, integrations, and reliable production workflows. Monitor and troubleshoot pipeline issues, support data-source onboarding, optimize platform performance and costs, and follow governance and security standards. Collaborate with BI developers, analysts, and data scientists while writing tested, documented code in an Agile environment.

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 Data Engineer designs, develops, tests, and supports cloud-based data solutions. Collaborates with Agile teams, mentors engineers, implements big data and streaming systems, and optimizes code for performance to deliver data-driven products.

Builds and operates scalable batch and real-time data pipelines (Databricks/Spark, Kafka), maintains Customer 360 data model and feature pipelines, ensures data quality/governance, delivers governed customer attributes to activation platforms, and supports privacy/compliance.