Top Senior Data Engineer Jobs

Build, optimize, and maintain data pipelines, ETL processes, data architectures, AI solutions, and analytics platforms. Develop cloud-based solutions using Azure Functions and containerization, implement CI/CD workflows, integrate databases and APIs, and ensure data quality, monitoring, reliability, and documentation. Troubleshoot production issues, collaborate with cross-functional teams, and apply modern data engineering, AI, cloud, and DevOps practices.

Build modern data and AI analytics solutions using ThoughtSpot, Snowflake, semantic data layers, and governed data assets. Develop ETL processes, AI prompts, intelligent dashboards, and interactive analytics experiences for actuarial teams. Partner with business and analytics stakeholders to support pricing, reserving, underwriting, and portfolio decisions while surfacing trends, explaining business drivers, and enabling actionable recommendations.

Own and evolve Altorfer’s enterprise data platform using Microsoft Fabric and related Microsoft technologies. Design and support Lakehouses, Warehouses, pipelines, integrations, and Medallion Architecture layers. Build ETL/ELT solutions connecting Dynamics 365, Dataverse, Power Platform, and other systems. Establish data governance, quality, lineage, security, monitoring, and performance standards. Troubleshoot production issues and support downstream analytics, AI, automation, Copilot, and reporting initiatives while partnering with technical and business stakeholders.

Manages and evolves enterprise data lakes and warehouses, designing scalable AWS-based batch, CDC, and near-real-time ETL/ELT pipelines. Builds data quality, governance, lineage, security, monitoring, and recovery controls; optimizes Spark, Glue, Athena, Redshift, and S3 workloads. Develops reusable Python, SQL, and PySpark frameworks, supports schema evolution and analytical data modeling, and collaborates with business and technical stakeholders to deliver reliable data solutions.

Design, build, and deliver scalable data pipelines and products end-to-end. Partner with analytics, product, and engineering to ingest diverse data sources, implement dbt transformations, ensure data quality, CI/CD, observability, monitoring, and cost/performance optimization. Leverage AI/automation for self-service tooling and intelligent pipelines while driving stakeholder alignment and reliable production operations.

Lead architecture and hands-on engineering to design, scale, and govern a cloud-native lakehouse using Databricks and Microsoft Fabric. Build batch/streaming pipelines, ensure HIPAA-compliant PHI handling, produce analytics-ready datasets, mentor engineers, and drive platform strategy, governance, and reliability.

Own and evolve Afresh’s analytics platform, including data architecture, dimensional models, semantic-layer metrics, pipelines, data quality, governance, and AI-assisted analytics tooling. Collaborate with data, product, engineering, and operations teams to support internal and customer-facing data products. Lead high-impact projects, optimize billion-row datasets, establish reliable analytics practices, and mentor other engineers.

Designs, builds, and maintains secure cloud data pipelines and centralized repositories; publishes curated datasets; develops WPS AI agents; produces dashboards and visualizations for senior military leadership; supports NATO and annual statutory reporting and prepares technical briefings and deliverables.

Lead hands-on data engineering and architecture for a classified on-prem Analytics Core: build ETL/ELT pipelines, clean and model 30 years of multi-modal and geospatial data, integrate ESRI spatial data, establish AI/ML feature standards, enforce DoD-aligned governance, and mentor transitioning staff to operationalize the solution.

Design and implement end-to-end data solutions on Microsoft Fabric and Azure for insurance datasets. Build ingestion pipelines (batch, streaming, API), Lakehouse architectures, and data models. Enforce governance, security, compliance, and cost/performance optimizations while guiding engineers and stakeholders on architectural best practices.

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.

Leads end-to-end data engineering solutions across multiple client engagements, from discovery and architecture through production support. Designs and optimizes Snowflake data platforms, models, warehouses, ELT/ETL pipelines, APIs, integrations, and deployments. Provides technical leadership, client communication, estimation, governance, performance and cost management, engineering standards, mentoring, training, and production escalation support.

Design, develop, deploy, operationalize, and maintain enterprise-scale data applications, pipelines, and platforms. Process structured and unstructured data, build scalable ETL and ELT workflows, convert data into usable formats, and support monitoring and operation of data platforms. Collaborate with analysts, developers, engineers, and data consumers in an agile environment to deliver mission-driven solutions.

Build strategic data infrastructure roadmaps, design and implement complex ETL/ELT pipelines, and deliver scalable, secure data solutions. Collaborate across teams on cloud platform selection, data management, governance, privacy, and compliance. The role requires expertise in Databricks, Snowflake, data modeling, performance optimization, and pipeline automation.

Leads the design, development, and operation of enterprise-scale data platforms and pipelines in Azure. Builds Lakehouse architectures using Databricks, Spark, Azure Data Factory, Synapse, and Data Lake Storage; implements data quality, monitoring, CI/CD, and governance practices; troubleshoots production issues; optimizes performance and scalability; collaborates with analysts, data scientists, and stakeholders; and mentors junior engineers.

Designs, builds, and maintains real-time and near-real-time data ingestion pipelines using Apache NiFi, REST APIs, scripting, and streaming technologies. Monitors data quality, pipeline health, observability, reliability, and performance while moving classified data across security domains. Develops automation for validation and alerting, documents data flows and procedures, and collaborates with engineering, platform, analytics, security, and governance teams to ensure compliant, trustworthy data delivery.

Develop and maintain large-scale, cloud-native data services and distributed pipelines for multi-tenant healthcare systems. Responsibilities include data modeling, database component development, SQL programming, performance tuning, testing, deployment, code reviews, proof-of-concepts, and full software development lifecycle ownership. Collaborate with global engineering teams, evaluate technologies, apply security and operational requirements, and use AI tools to accelerate development.

Lead data engineering projects by designing scalable data solutions, pipelines, ETL processes, cloud-native applications, and ML-enabled analytical tools. Responsibilities include architecture design, code reviews, CI/CD, documentation, mentoring engineers, client engagement, proposal support, and collaboration with consulting teams. The role requires strong Python, SQL, cloud, big-data, orchestration, and DevOps expertise, with international client travel as needed.

Design and implement scalable data pipelines and a SOC2-compliant data warehouse. Support analytics and ML teams, enable real-time and batch ETL, mentor data engineers, and collaborate cross-functionally to drive data-driven decisions.

Lead data engineering initiatives involving large-scale data solutions, cloud-native applications, ETL pipelines, data architectures, CI/CD, and machine learning deployment. The role includes mentoring engineers, ensuring code quality, documenting operational processes, supporting consulting teams, engaging clients, contributing to proposals, and delivering analytics solutions globally. International client travel may be required.

Design, build, and operate scalable real-time and batch data pipelines ingesting high-frequency telemetry, simulation, and trackside data. Own Kafka/Flink streaming components, Databricks lakehouse implementations, and infrastructure-as-code deployments. Develop software in Python/Java/SQL, integrate systems, automate testing and releases, performance-tune pipelines, and mentor peers to ensure resilient, secure, high-performance data delivery for motorsports teams.

Designs and deploys end-to-end data infrastructure, including schemas, warehouses, automated pipelines, data quality monitoring, and curated datasets. Builds Power BI solutions for self-service analytics and partners with product, marketing, sales, data science, and machine learning teams. Applies analytical techniques, productionizes machine learning and LLM use cases, and translates complex data into business insights.

Design, build, and own scalable ETL/ELT pipelines, data models, and data infrastructure. Improve data quality, observability, performance, and CI/CD for data workflows. Partner with analytics, ML, software, and product teams, mentor engineers, and resolve complex data issues.

Design, build, and maintain scalable batch and real-time data pipelines and data lake architecture. Improve observability and SLOs, optimize ETL/ELT, develop dbt workflows, support event-driven architectures, integrate financial APIs, and collaborate with analytics and ML teams to deliver reliable, model-ready data products.

Designs, builds, and maintains scalable data pipelines, data marts, enterprise data platforms, and ETL processes. Integrates diverse data sources, improves data quality and performance, and supports secure analytics infrastructure using Microsoft Fabric and Azure. Develops Tableau and Power BI dashboards, collaborates with cybersecurity and cross-functional teams, manages project deliverables, documents processes, and trains team members on data organization and interpretation.