Top Data Engineer Jobs in Dallas, TX

Design, build, and maintain scalable batch and streaming data pipelines on GCP using Dataflow, Airflow, Pub/Sub, and BigQuery. Develop analytical data models, integrations, cloud infrastructure, CI/CD workflows, observability, governance, and secure data platforms at terabyte-to-petabyte scale. Lead architecture decisions, mentor junior engineers, and create reusable datasets and catalogs. Also support enterprise AI/ML platforms, including LLM and generative AI integrations, model deployment, monitoring, governance, and lifecycle management.

Lead the design and optimization of enterprise data platforms, integrating healthcare and other business systems. Develop high-performance SQL Server solutions, tune T-SQL and ETL workloads, build Azure Data Factory pipelines, and apply Python and AI/LLMs for data extraction. Responsibilities include data modeling, profiling, cleansing, lineage, master data management, monitoring, automation, stakeholder collaboration, mentoring junior engineers, and establishing data engineering best practices.

Leads development of Navanta’s secure data backbone, including lakehouse architecture, bank-system ingestion, CDC pipelines, semantic modeling, data quality, reconciliation, observability, governance, and lineage. Partners with AI/ML, security, and platform teams to deliver reliable, queryable data products for financial institutions. Owns data contracts, access controls, regulatory data handling, pipeline SLAs, bank onboarding, and on-call reliability operations.

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.

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.

Design and maintain scalable cloud data pipelines, ELT/ETL workflows, data models, ingestion frameworks, and automation systems supporting cloud cost visibility and optimization. Optimize performance, reliability, and cost across AWS, Snowflake, and related platforms. Ensure data quality, validation, auditability, and production troubleshooting while partnering with Engineering and Finance stakeholders on usage-based insights and cost-aware decisions.

Design and maintain scalable cloud data pipelines, ELT/ETL workflows, data models, and automation systems supporting cloud cost visibility and optimization. Use Python, SQL, dbt, Airflow, AWS services, and Snowflake to process telemetry and usage data. Optimize performance, reliability, storage, and compute costs while ensuring data quality, auditability, and validation. Partner with Engineering and Finance stakeholders on cost-aware decision-making and usage insights.

Designs, builds, and operates scalable enterprise data platforms supporting ingestion, processing, storage, governance, analytics, and AI/ML. Responsibilities include developing batch and real-time pipelines, lakehouse and warehouse architectures, orchestration, infrastructure automation, CI/CD, monitoring, security, cost optimization, and observability. The role partners with data, ML, DevOps, security, and application teams, troubleshoots complex platform issues, establishes reusable engineering standards, contributes to architecture strategy, and mentors engineers.

Designs and delivers scalable, low-latency streaming data pipelines using Apache Flink, Confluent Kafka, and Apache Iceberg. Modernizes batch and RDBMS workflows into event-driven architectures, builds lakehouse data structures, ensures data quality, optimizes streaming performance, and integrates ITSM and enterprise systems. The role also leads testing, documentation, delivery lifecycle activities, stakeholder collaboration, CI/CD, operational monitoring, and mentoring of data engineers.

Build and scale data infrastructure supporting utility billing reports, analytics, AI-ready retrieval, dimensional models, and customer-facing data products. Responsibilities include developing ingestion and transformation pipelines, creating facts and dimensions, implementing testing and observability, monitoring freshness and lineage, and partnering with engineering, product, compliance, and analytics teams.

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, build, test and support batch and streaming data pipelines and curated datasets on a modern Lakehouse and AI data platform. Responsibilities include data modelling, optimisation, quality controls, reconciliation, platform tooling, and partnering with stakeholders to deliver reliable, production-ready data products.

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.

Build and scale a client-facing analytics platform using Microsoft Fabric, scalable pipelines, Lakehouse architectures, dimensional models, and Power BI semantic models. Establish ingestion, observability, testing, CI/CD, and SDLC standards while optimizing performance, refresh strategies, costs, and SLAs. Lead architecture decisions, communicate technical concepts to business stakeholders, mentor engineers, and proactively manage platform risks in a client-facing analytics environment.

Design, build, optimize, and maintain scalable data pipelines and platform components supporting analytics and AI/ML. Own complex engineering initiatives, resolve performance and production issues, improve governance and data models, and contribute to infrastructure as code and CI/CD. Provide technical leadership through architecture guidance, code reviews, mentoring, documentation, and cross-functional collaboration. Support platform modernization across cloud data platforms, warehouses, lakehouses, and streaming or feature-store use cases.

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.

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, 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.

Senior Data Engineer responsible for designing, building, and optimizing enterprise data pipelines, architectures, and scalable reporting solutions. The role integrates data from diverse business platforms, develops Snowflake and dbt solutions, implements governance and security, and enables self-service analytics. It partners with executives, product teams, analysts, developers, and data scientists while mentoring team members, evaluating emerging technologies, and delivering reliable data assets and actionable insights.

Performs data-quality checks, migration validation, SQL-based reconciliation, discrepancy investigation, and root-cause analysis across large VA healthcare datasets. Supports DMM tools, cutover and go-live activities, reporting, application monitoring, access administration, documentation, and stakeholder meetings. Collaborates with VA, Oracle Health, InterSystems IRIS, engineering, and subject-matter teams to resolve migration issues and implement new data-quality capabilities.

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.

Leads enterprise data engineering and AI-ready data knowledge initiatives across Network Services. Designs scalable pipelines, data models, metadata, governance, lineage, quality controls, and validation frameworks for structured and unstructured data. Partners with subject matter experts to prepare trusted context for AI and agentic workflows, guides hybrid data architectures, supports testing and releases, establishes control standards, and mentors data engineers.