Top Senior Data Engineer Jobs

Designs, builds, and maintains scalable data pipelines, warehouses, and lakes integrating complex network operations data. Uses Kafka, Elastic Stack, AWS, OpenShift, Kubernetes, and automation to support analytics, reporting, and operational dashboards. Responsibilities include ETL/ELT, data quality, performance optimization, CI/CD, security compliance, troubleshooting, and documentation. Collaborates with analysts and visualization engineers in an Agile federal IT modernization environment.

Build and manage scalable ETL/ELT pipelines using Microsoft Fabric and Azure Databricks, implement medallion architecture, support data migrations and legacy SSRS decommissioning, develop Power BI data models, and maintain governance, security, and CI/CD practices. The role also supports migration events and modernizes data infrastructure for advanced analytics and reporting.

Build and maintain centralized, validated data platforms covering reference, fundamental, market, index, exchange, instrument, and security master datasets. Analyze vendor data, design scalable data models, implement quality controls, resolve processing issues, and partner with quantitative researchers and traders. Provide technical leadership and mentorship while maintaining testing and system design standards across the Core Data team.

Build and maintain the Air Force Data Mesh Environment through scalable ETL and ELT pipelines, data models, orchestration workflows, APIs, and governed data applications. Develop secure, maintainable software using Python, TypeScript, React, Node.js, SQL, and modern data tools. Collaborate with product, DevOps, security, and mission stakeholders while applying testing, validation, observability, and secure data-handling practices.

Designs and maintains scalable cloud data architectures, pipelines, ETL processes, data warehouses, databases, and data models. Ensures data quality, accuracy, security, availability, and consistency across sources. Develops SQL queries for analytics and reporting, and documents pipelines, schemas, architectures, and models. The role requires expertise with Python, Java, Scala, R, SQL, relational and NoSQL databases, cloud platforms, and Snowflake.

Build and maintain scalable ETL/ELT pipelines and data workflows using Python, PySpark, and SQL. Improve data quality, reliability, architecture, and governance while supporting analytics, reporting, and machine learning initiatives. Collaborate with data scientists, analysts, and business stakeholders, use Azure DevOps for CI/CD and release management, and mentor junior engineers. The role also involves API integration, cloud data platforms, testing, version control, and contributing to engineering best practices.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Build and maintain Snowflake data models using dbt, improve data quality and observability, define data contracts, and support a modular healthcare data platform. Collaborate with analysts, data scientists, and software engineers to deliver trusted datasets. Responsibilities include pipeline monitoring, incident troubleshooting, performance and cost optimization, design reviews, documentation, and knowledge sharing. The role requires strong SQL and Python, data modeling, orchestration, CI/CD, cloud data warehouse, security, governance, and healthcare data knowledge.

Build and maintain Snowflake data models using dbt, improve data quality and observability, define data contracts, monitor pipelines, troubleshoot incidents, optimize data platform performance and costs, and document scalable data engineering practices. Collaborate with analysts, data scientists, software engineers, and other stakeholders while supporting healthcare data configurations and privacy requirements.

Design, build, and maintain large-scale ETL pipelines and data warehouses (Redshift) with strong data modeling, governance, and quality controls. Optimize systems for performance and reliability, integrate and deploy AI/ML models, troubleshoot and support Redshift, collaborate with data scientists and stakeholders, and produce technical documentation and knowledge-base articles.

Designs and manages scalable data pipelines, contracts, transformation layers, and analytics models. The role focuses on data governance, observability, lineage, configuration validation, and collaboration with product engineering teams. It requires processing large datasets, integrating cloud and on-premises industrial technologies, and implementing streaming and message-queuing solutions, ideally in cybersecurity or industrial environments.

Designs and maintains scalable data architecture, warehouses, ETL/ELT pipelines, and data platforms. Responsibilities include ensuring data quality, integrity, governance, accessibility, reliability, and performance; supporting customer onboarding and analytics tools; contributing to architecture and engineering best practices; troubleshooting complex data issues; and participating in on-call rotations. The role collaborates with engineering, product, solutions, business intelligence, and other cross-functional teams in healthcare and social care data environments.

Lead enterprise-scale marketing data engineering initiatives, designing data products, pipelines, lakehouse architectures, semantic models, governance frameworks, and AI-ready platforms using Microsoft Fabric and Azure. Partner with stakeholders to translate business needs into scalable solutions, ensure data quality and security, drive CI/CD and infrastructure automation, and mentor engineers. Own data architecture, domain data products, technical backlogs, platform modernization, and engineering standards across the data lifecycle.

Builds and maintains production-grade ETL/ELT pipelines across legacy on-premise systems and AWS/Azure cloud platforms, centralizing data in Snowflake for analytics, machine learning, feature stores, and generative AI. Develops vectorization and embedding pipelines, optimizes Snowflake performance and cloud costs, manages cross-platform orchestration, and implements data quality and observability frameworks. Partners with IT, security, MLOps, data science, and infrastructure teams to deliver scalable, governed data solutions.

Own the analytics data layer by translating stakeholder needs into certified dimensional models, governed metrics, semantic layers, dashboards, and reports. Build with dbt, SQL, Databricks, Power BI, and Airflow while ensuring data quality, observability, reconciliation, documentation, and dependable orchestration. Partner closely with finance, operations, and client-facing teams, automate recurring workflows, and use AI-assisted engineering practices in a fully remote environment.

Design and build resilient data pipelines integrating files, APIs, services, databases, and mission sources. Develop extraction, normalization, enrichment, validation, cataloging, routing, ingestion, and event-driven workflows. Preserve geospatial metadata, provenance, lineage, security attributes, and data quality. Apply AI, RAG, MCP, and agentic capabilities to improve data discovery and access. Build Node.js applications and Oracle Database integrations while collaborating with engineering, cloud, security, AI, and mission teams.

Design, build, and maintain ETL/ELT pipelines and API integrations to ingest, cleanse, transform, and analyze infrastructure and cloud data. Produce analytics, dashboards, and executive reports; automate workflows and support near-real-time processing. Follow SDLC/DevOps practices (Git, CI/CD), ensure data governance, quality, lineage, and support incident, release, and compliance activities.

Build scalable financial data infrastructure for payment, invoicing, ledger, and transaction systems. Architect Kafka-based event-driven pipelines with transactional integrity, develop data models, automate ETL/ELT processes, and improve data quality and reliability. Design REST APIs and cloud services using PostgreSQL, Databricks, dbt, and lakehouse technologies. Collaborate across teams to automate financial operations and create resilient, self-monitoring distributed systems.

Build, deploy, and operationalize complex data pipelines and solutions using Databricks, cloud platforms, Python, and SQL. Responsibilities include data transformation, cleansing, governance, security, quality, source analysis, testing, defect resolution, and support for AI, machine learning, and business intelligence. The role also involves cross-team collaboration, stakeholder education, and delivery of resilient data products.

Own the enterprise product data architecture for Allegion’s Global Software Solutions business. Define unified data models, schemas, pipelines, APIs, catalogs, governance standards, and device telemetry architecture across multiple business units. Lead data onboarding, platform convergence, architecture audits, compliance, and engineering enablement. Partner with enterprise data, AI, identity, and product engineering teams to make governed product data usable for analytics and agentic AI.

Designs, builds, and operates scalable batch and streaming data pipelines, ELT/ETL workflows, and cloud data infrastructure. Ensures data reliability, quality, observability, lineage, performance, and cost efficiency across enterprise domains. Partners with analytics engineers, BI teams, and business stakeholders to deliver analytics-ready datasets. Establishes engineering practices including version control, CI/CD, automated testing, infrastructure-as-code, architecture standards, and documentation.

Architects, builds, and scales data pipelines, ETL processes, data models, and platforms using dbt, Snowflake, Airflow, and Fivetran. Leads data engineering best practices including CI/CD, data quality, governance, and observability. Collaborates with Data Science, Engineering, Product, and business stakeholders; mentors engineers and data scientists; contributes to architecture and technical decisions; and participates in on-call support.

Designs, builds, deploys, and supports scalable Databricks data pipelines and lakehouse solutions for observability, infrastructure, and security data. Develops PySpark and Structured Streaming workloads, establishes data quality and governance standards, optimizes performance, and improves reliability and operational excellence. Partners with security, infrastructure, observability, and platform engineering teams to onboard data sources, implement access controls, advance CI/CD practices, and deliver trusted enterprise data products.

Designs, builds, and maintains scalable Azure data pipelines and ELT frameworks using Databricks, SQL, Python, and PySpark. Implements Azure DevOps CI/CD, monitoring, security, infrastructure automation, and production support. Collaborates with BI teams on Power BI datasets, documents data solutions, participates in Agile delivery, and mentors junior engineers. The role requires strong data modeling, structured and semi-structured data expertise, performance tuning, and data governance awareness.

Build and optimize enterprise-scale Snowflake data solutions, including ingestion, ETL pipelines, transformation, quality assurance, production deployment, legacy warehouse migration, and workflow automation. Establish data governance, compliance policies, and data quality rules while supporting cloud-based architectures and efficient data consumption.