Top Senior Data Engineer Jobs in Dallas, TX

Designs, builds, and operates scalable, resilient cloud data platforms and pipelines. Leads large-scale data initiatives, makes architectural decisions, evaluates technologies such as Snowflake and Databricks, and develops solutions using distributed data systems, relational and NoSQL databases, and cloud platforms. Collaborates with product and engineering teams, establishes reusable design patterns, communicates technical concepts, and mentors data engineers, analysts, and scientists.

Design and develop data pipelines, integrations, transformations, and infrastructure across AWS, Snowflake, Airflow, and MySQL. Troubleshoot warehouse issues, validate data quality, improve pipeline reliability, and translate business needs into data engineering solutions. Interpret technical findings for client recommendations, manage workstreams, review junior team members’ work, and provide technical guidance across managed services engagements.

Designs, builds, and operates scalable cloud data platforms, APIs, pipelines, and data products. Leads architectural decisions, evaluates platforms such as Snowflake and Databricks, develops distributed and streaming workloads, enforces engineering standards, and mentors data engineers. Collaborates with product managers, software engineers, analysts, and data scientists to deliver resilient solutions. Requires extensive experience with application development, distributed data, SQL, programming, data pipelines, data modeling, cloud platforms, and relational and NoSQL databases.

Leads enterprise data engineering strategy, architecture, modernization, and governance. Designs scalable cloud-native data lakes, warehouses, lakehouses, ingestion pipelines, and real-time processing platforms. Establishes standards for data modeling, security, quality, observability, DataOps, DevOps, and compliance. Provides technical leadership across engineering teams, mentors senior professionals, drives transformation programs, and partners with business and technology stakeholders to deliver resilient data products supporting analytics, regulatory reporting, and operational intelligence.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Lead architectural decisions, establish reusable engineering patterns, evaluate technologies such as Snowflake and Databricks, and deliver distributed, real-time, and warehouse solutions. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical outcomes to stakeholders.

Develop and support SQL Server and Snowflake data platforms, build ETL/ELT pipelines, maintain data models, optimize performance and reliability, and deliver analytics datasets. The role partners with business and technical teams, supports CI/CD and automated deployments, and ensures data governance, security, HIPAA compliance, and data integrity across clinical, operational, financial, and external data sources.

Build and architect scalable cloud data solutions, pipelines, platforms, and data products. Lead large-scale data initiatives, make architecture and platform decisions, ensure performance and operational efficiency, and apply modern technologies such as Python, Spark, Databricks, and Snowflake. Collaborate with Agile teams, product managers, software engineers, analysts, and data scientists while mentoring engineers and communicating technical concepts to stakeholders.

Leads end-to-end data engineering solutions, including pipeline design, data modeling, testing, deployment, optimization, production support, and data quality. Partners with stakeholders to develop cloud-based data platforms, prototypes, technical standards, and scalable analytic systems. Provides technical leadership, conducts design and code reviews, mentors team members, and ensures compliance with risk and data management policies.

Designs, builds, and supports enterprise data products, analytics platforms, scalable pipelines, Lakehouse architectures, and integration solutions. The role integrates operational and enterprise systems, develops governed and reusable data products, supports reporting and analytics, and implements data quality, validation, monitoring, lineage, and modeling processes. Strong Databricks, SQL, Python, Azure, and Power BI expertise is required, with experience in operationally intensive environments such as manufacturing, IoT, supply chain, or logistics.

Builds and maintains enterprise data warehouse solutions, relational database schemas, ETL pipelines, and automated data processes. Responsibilities include optimizing complex SQL queries, indexing, execution plans, data modeling, governance, system support, and cross-functional delivery. Requires strong SQL, SSIS, data warehouse, and relational database expertise, with preferred experience in Azure Data Factory, ActiveBatch, and Snowflake.

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.

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.

Designs trusted Snowflake data products and enterprise semantic models that standardize business concepts, metrics, relationships, and rules. Builds Cortex semantic views and agents, validates AI-generated results, develops regression tests, and maintains metadata, lineage, and documentation. Leads modeling initiatives from discovery through production while partnering with business and technical stakeholders to improve analytics, automation, and AI consistency.

Build and support AWS data lake and Snowflake solutions, develop Python and Spark data pipelines, automate infrastructure with Terraform, and manage CI/CD deployments. The role uses AWS services including Glue, S3, Lambda, Step Functions, and Lake Formation, along with monitoring and change-management tools. Candidates will also apply AI-assisted engineering tools to improve productivity and may work with Databricks, Unity Catalog, and cloud analytics platforms.

Leads the architecture, development, and delivery of enterprise data platforms using Snowflake, AWS, Python, Spark, Informatica, and modern ELT frameworks. Designs Data Vault and dimensional models, scalable ingestion pipelines, and cloud data architectures. Provides technical leadership, mentors engineers, manages delivery across initiatives, and ensures data quality, performance, governance, monitoring, and production reliability. Partners with business and technical stakeholders to translate requirements into robust data solutions.

Lead the design, development, deployment, and operation of scalable Databricks batch and streaming data solutions. Build governed ingestion, transformation, quality, observability, and analytics pipelines using Python, PySpark, Spark, SQL, Delta Lake, and Unity Catalog. Establish engineering standards, optimize production workloads, implement infrastructure as code and CI/CD, support AI and machine learning data products, resolve technical issues, lead design reviews, and mentor engineers.

Designs, develops, and maintains scalable AWS data platforms, data lakes, and pipelines using Glue, S3, Lambda, Step Functions, Python, Snowflake, and Terraform. Implements governance and security through Lake Formation, automates infrastructure and deployments with CI/CD, and monitors production workloads using CloudWatch and Splunk. Collaborates with engineering and architecture teams to deliver reliable data solutions, leveraging modern AI-assisted development tools.

Leads end-to-end data enablement for Supply Chain and Manufacturing domains. Partners with business and technical stakeholders to define use cases, roadmaps, data models, semantic layers, integration requirements, quality controls, and governed data products. Builds and optimizes Databricks lakehouse pipelines using SQL, Python, PySpark, and Delta Lake across complex ERP, manufacturing, procurement, inventory, and finance systems. Establishes standards for lineage, security, documentation, reliability, and production readiness.

Own data engineering delivery for multiple clients, including designing and maintaining SQL and Python ETL/ELT pipelines, integrations, data warehouses, and Airflow orchestration. Translate business requirements into scalable data solutions, manage scope and priorities, troubleshoot data integrity and performance issues, and establish quality controls and documentation. Serve as a client-facing technical lead, guide architecture through influence, and use AI-assisted coding tools while supporting team best practices.

Lead the design and development of scalable data pipelines, microservices, cloud-native ingestion systems, and streaming solutions. Collaborate with architects, engineers, product teams, and business stakeholders to build reliable data platforms using Python, Java, AWS services, SQL, and relational and NoSQL databases. Establish data quality, testing, monitoring, automation, and validation frameworks while optimizing performance and resolving production issues across distributed systems.

Designs and maintains cloud-based data ingestion, discovery, and consumption pipelines across APIs, files, CDC, and streaming sources. Owns data-source onboarding, modeling, quality, privacy, lineage, testing, orchestration, monitoring, documentation, and production troubleshooting. Collaborates with product owners, engineering leads, and data stewards to build secure, governed Lakehouse data products. Applies software engineering, AI-assisted development, CI/CD, DevOps, and infrastructure-as-code practices.

Build and maintain scalable data pipelines and distributed processing solutions supporting analytics, reporting, and machine learning. Develop cloud-based Big Data platforms using Python, Spark, Hadoop, and AWS or GCP. Apply data quality, governance, monitoring, and automation practices; collaborate with architects, data scientists, and analytics teams; participate in Agile delivery and production support; and mentor junior engineers.

Develop and maintain Power Automate RPA workflows and enterprise integrations. Build Python automation, ETL processes, Azure and Snowflake data pipelines, data models, quality frameworks, and monitoring solutions. Create Power BI semantic models, reports, DAX measures, and security configurations. Support streaming, API, and application integrations across Azure, Snowflake, Databricks, Kafka, and related platforms.

Develop and manage enterprise data pipelines and dbt transformations in Snowflake, from source ingestion through data marts. Responsibilities include data integration, modeling, testing, observability, security, performance tuning, troubleshooting, documentation, metadata management, and architecture. The role collaborates with technical and business stakeholders to deliver reliable, governed, high-quality data products and continuously improve the data platform.

Designs, develops, and maintains scalable AWS data platforms, data lakes, and pipelines. Builds workflows with Glue, S3, Lambda, and Step Functions; implements governance and access controls through Lake Formation; automates infrastructure with Terraform; and develops Python and Boto3 integrations. Manages Snowflake platforms, CI/CD deployments, testing, monitoring, troubleshooting, and production optimization using CloudWatch and Splunk. Collaborates with engineering and architecture stakeholders while using AI-assisted development tools.