Top Senior Data Engineer Jobs

Design, develop, and optimize enterprise-scale data platforms, pipelines, data lakes, warehouses, and analytical datasets on Azure and Snowflake. Build ETL/ELT processes using ADF, Databricks, PySpark, SQL, Python, and Snowflake. Manage data models, governance, security, quality, CI/CD, cloud migrations, and performance optimization. Collaborate with stakeholders, troubleshoot production issues, document solutions, and support Agile delivery.

The Senior Data Engineer will design, implement, and manage data pipelines for real-time analytics and ML training datasets, ensuring efficient data flow and quality in a hybrid environment.

Develop and operationalize data applications using Palantir Foundry for national security clients. Translate stakeholder needs into technical solutions, ingest and transform data, build workflows and applications, support scalable data platforms, and lead analytics projects. The role also involves advising clients on data-driven mission impacts, collaborating with Agile teams, and applying DevSecOps, AI/ML integration, security, and compliance practices.

Build and operationalize data pipelines and solutions supporting AI, machine learning, and business intelligence. Responsibilities include designing data solutions, analyzing and cleansing data, implementing governance and security practices, testing transformations, resolving defects, and collaborating across teams. The role requires Python, SQL, AWS, Databricks, Terraform, API design, and AI platform experience.

Build and operate core data infrastructure for edge and hybrid environments, including ingestion, OT/IoT integrations, real-time streaming, data contracts, schemas, validation, lineage, observability, telemetry, and scalable query access. The role requires production ownership, reliability practices, customer integration, and collaboration across engineering and customer-facing teams. Candidates should have 6+ years of experience with production data, streaming, or integration systems and proficiency in Python and/or Go.

Own Orum’s data platform architecture, roadmap, reliability, and scalability. Build batch and real-time ETL/ELT pipelines using PostgreSQL, event streams, ClickHouse, BigQuery, and dbt. Administer Looker and develop LookML models, dashboards, and a trusted semantic layer. Establish data quality, governance, lineage, observability, and metric standards while partnering with Product, Finance, GTM, and Operations on analytics, AI, and business intelligence initiatives.

Build and maintain scalable data platforms, pipelines, and ETL/ELT workflows for structured and unstructured data. Develop scripts and programs to convert data into usable formats, support platform operations, and collaborate with analysts, engineers, developers, and data consumers in agile environments. Projects support missions including fraud detection, cancer research, and national intelligence.

Builds and maintains reliable, scalable, secure data platforms and cloud infrastructure. Responsibilities include troubleshooting Tier 3 production issues, designing data ingestion and transformation workflows, implementing CI/CD, monitoring systems, and supporting enterprise data operations. The role may involve Kubernetes, Kafka, NiFi customization with Java, AWS services, and classified Intelligence Community environments.

Designs and operates batch, streaming, and real-time data platforms and pipelines using Spark, Kafka, Iceberg, Airflow, and cloud infrastructure. Owns data lake evolution, data quality, observability, reliability, governance, event instrumentation, schema management, and privacy controls for PII, retention, deletion, and residency. Leads complex initiatives end-to-end, troubleshoots production issues, mentors engineers, and develops AI agent harnesses that enforce data engineering standards and quality gates.

Designs and operates scalable batch and streaming data pipelines and AI-ready data products across Databricks, Snowflake, Kubernetes, and cloud services. Develops governed data assets, semantic models, search and retrieval capabilities, and data quality processes. Supports Snowflake Cortex and Databricks initiatives, production troubleshooting, orchestration, performance optimization, CI/CD, and observability. Partners with business and technical teams on roadmaps and adoption, evaluates AI-assisted engineering tools, mentors engineers, and establishes reusable platform patterns.

Develop and maintain ETL pipelines integrating systems such as Canvas and Qualtrics; automate data-quality and analytics workflows using Python, SQL, and cloud-based tools. Diagnose data issues, create business intelligence solutions, translate stakeholder needs into analytical requirements, and document and train users on implemented systems. The role also supervises student employees and supports reporting, surveys, accreditation, and process improvement.

Designs, builds, and manages a Snowflake-based business intelligence platform using dbt, SQL, data modeling, pipelines, and transformation tools. Performs business and systems analysis, fit-gap analysis, security and permissions testing, technical specifications, solution development, system and user acceptance testing, troubleshooting, documentation, training, and risk management. Collaborates with reporting, functional, PeopleSoft, database, security, and other project teams to modernize citywide analytics and data sharing.

Lead the architecture, development, governance, and operation of scalable data platforms, pipelines, schemas, databases, and warehousing infrastructure. Guide data acquisition, transformation, cataloging, and integration across cloud and local environments while ensuring reliability, security, performance, and data quality. Collaborate with product and engineering leadership, recommend data solutions, establish technical standards, and mentor engineering teams.

Lead the design, development, deployment, and operation of scalable data pipelines, APIs, services, and healthcare analytics applications. Translate business and analytical requirements into reliable production solutions, establish technical standards and architecture, troubleshoot issues, improve performance, document designs, coordinate platform changes, and provide technical leadership and mentorship to data engineers.

Build and operate a scalable data platform supporting pipelines, reporting, and analytics across business teams. Responsibilities include defining canonical data models, implementing workflow orchestration with tools such as Airflow, improving platform infrastructure, troubleshooting complex data and system issues, enforcing data security controls, and mentoring junior engineers. The role requires strong software engineering, cloud, infrastructure, communication, and data pipeline expertise.

Designs and evolves an enterprise Azure-based Lakehouse platform using Microsoft Fabric and Azure Databricks. Builds metadata-driven ingestion, ELT, CDC, orchestration, Spark/PySpark transformations, semantic models, and governed Bronze, Silver, and Gold data layers. Integrates enterprise systems across ERP, CRM, construction, manufacturing, HR, and operations while establishing standards for data quality, lineage, security, observability, performance, and cost optimization. Partners with stakeholders to deliver trusted data products and provides technical leadership across architecture and engineering.

Designs and maintains scalable infrastructure for ingesting, processing, storing, and serving large volumes of legal and financial document data. Responsibilities include building data pipelines and backend services, improving databases and schemas, implementing governance and GDPR compliance, supporting machine learning workloads, leading technical initiatives, and mentoring engineers.

Leads multidisciplinary data engineering and research projects supporting sleep physiology and wearable-device modeling. Designs and maintains multimodal data pipelines, ETL workflows, schemas, databases, cloud infrastructure, and machine-learning data products. Oversees data ingestion, cleaning, performance optimization, governance, documentation, and regulatory support while mentoring engineers and analysts. Translates scientific hypotheses into scalable engineering solutions, communicates progress to leadership, and provides technical oversight across the full data ecosystem.

Builds and maintains scalable data pipelines, ETL infrastructure, data warehouses, and analytics tools. The role assembles complex datasets, automates processes, optimizes data delivery architecture, and supports cross-functional data initiatives. Collaborates with executive, product, data, and design stakeholders to resolve technical issues and improve analytics systems. Requires proficiency in SAS, Python, Java, MATLAB, SQL, NoSQL, Hadoop, MapReduce, Hive, Presto, UNIX, and Linux, with JavaScript, R, and basic machine learning preferred.

Leads the design, development, and implementation of scalable data pipelines, APIs, cloud-based integrations, and Customer Data Platform solutions. Builds unified customer data ecosystems supporting analytics, personalization, and enterprise integration. Responsibilities include data modeling, Lakehouse and API optimization, governance, privacy compliance, CI/CD automation, monitoring, troubleshooting, documentation, and cross-functional collaboration. Requires extensive experience with Python, SQL, REST APIs, cloud platforms, data integration tools, CDPs, and modern data engineering architectures.

Build and scale analytical data products, models, semantic layers, and reusable metrics using dbt and Databricks. Develop Gold-layer and dimensional models, support Tableau analytics, automate data workflows with Python and SQL, and implement testing, documentation, lineage, Git, CI/CD, and deployment practices. Provide technical leadership, architecture guidance, mentorship, code reviews, performance optimization, and production support while partnering with business, product, analytics, and engineering teams.

Own NimbleRx’s data platform end to end, including ingestion, transformation, storage, querying, access, batch and streaming pipelines, warehouse modeling, security, compliance, and reliability. Optimize PostgreSQL and Java/Spring data-layer performance, investigate infrastructure issues, develop AI-enabled tooling, partner across teams, and mentor engineers and analysts.

Design, develop, and maintain data pipelines and software solutions to process and combine diverse data sources. Perform statistical analysis (regression, anomaly detection, clustering), automate and optimize data products, communicate findings to technical and non-technical stakeholders, elicit system requirements, validate performance, and support time-sensitive intelligence operations.

Lead the architecture and development of Vercel’s Finance Data Platform supporting revenue recognition, consumption-based billing, forecasting, reporting, and compliance. Build reliable batch and real-time pipelines using Kafka, ClickHouse, Tinybird, and Snowflake; establish governance, lineage, auditability, monitoring, and fault tolerance; translate Finance and Accounting requirements into scalable systems; guide architecture and roadmap decisions; collaborate with Security, Legal, Compliance, Audit, FP&A, and RevOps; and mentor data engineers.

Lead the architecture, development, and optimization of Snowflake and enterprise data platforms. Design scalable ETL/ELT pipelines, dimensional data models, and medallion architectures while establishing engineering standards for testing, deployment, monitoring, security, and data quality. Partner with stakeholders on technical roadmaps, mentor engineers and analysts, conduct design and code reviews, modernize legacy pipelines, and deliver reliable, reusable data solutions.