Top Senior Data Engineer Jobs

Designs and optimizes scalable data pipelines, Snowflake data warehouse solutions, and dbt transformation frameworks. Develops dimensional and enterprise data models, ETL/ELT processes, quality tests, documentation, and governance practices. Tunes Snowflake performance and costs, supports migrations and integrations, and collaborates with stakeholders, analysts, architects, and engineers to deliver analytics and reporting solutions.

Lead enterprise data platform architecture across ingestion, storage, processing, governance, security, and consumption. Establish standards for data modeling, lakehouse and streaming patterns, pipelines, lineage, cataloging, disaster recovery, and cost optimization. Partner with engineering, ML, BI, product, and business teams; lead architecture reviews, major platform initiatives, and mentoring while producing architecture artifacts and guiding scalable, reliable data infrastructure.

Design and build PerfectServe’s unified cloud data platform, including ingestion pipelines, integrations, data models, metrics, and consumer-facing data layers. Establish architecture, engineering, quality, and testing standards; enable safe extraction from production systems; partner with data users; and mentor data engineers. The role requires substantial cloud data platform experience, deep SQL, warehouse expertise, ELT, change-data-capture, AWS, modeling, orchestration, and data quality skills.

Owns enterprise data architecture and scalable Databricks pipelines using medallion architecture and lakehouse formats. Designs dimensional models, star schemas, data marts, and the semantic layer; governs data quality and reliability; optimizes infrastructure and cloud costs; prioritizes deployments; collaborates with stakeholders; and mentors a junior data engineer.

Build and operate production data pipelines that ingest, transform, validate, and deliver messy clinical data for machine learning, analytics, and clinician-facing APIs. Own data contracts, monitoring, backfills, failure recovery, and troubleshooting across systems. Collaborate with integration, ML, and product engineering teams to ensure reliable data delivery across hospital deployments.

Designs, develops, and maintains large-scale data processing systems, including ETL processes, data models, scalable pipelines, databases, data warehouses, and cloud-based big data solutions. Uses Snowflake, Fivetran, dbt, and Airflow while applying DevOps and security practices. Optimizes distributed processing and storage systems, collaborates with data scientists and analysts, and mentors junior engineers.

Develops and supports Oracle database solutions, including complex SQL and PL/SQL objects, performance tuning, security, data governance, quality management, and migration initiatives. Performs ETL/ELT, data validation, reconciliation, conversion, and post-migration verification while maintaining metadata, lineage, documentation, and data dictionaries. Collaborates with data owners, developers, DBAs, business users, and project managers. Requires five or more years of Oracle development experience and an active TS/SCI clearance with polygraph.

Designs, builds, operates, and improves large-scale data pipelines and foundational data products supporting healthcare analytics, customer applications, and AI/ML use cases. Responsibilities include transforming healthcare datasets, improving reliability and performance, implementing data quality and observability, debugging production workflows, contributing to architecture and CI/CD, and collaborating across engineering and product teams. The role also involves mentoring, technical design reviews, and production support.

Leads the architecture, development, governance, and operational excellence of AssetMark’s Azure and Snowflake data platform. Designs scalable ETL/ELT pipelines, optimizes performance and costs, establishes DataOps, observability, lineage, security, and SLAs, and mentors engineers. Partners with data science and product teams to enable machine learning, MLOps, feature engineering, and generative AI capabilities. Provides hands-on coding in Python and SQL while guiding technical strategy, standards, code reviews, CI/CD, and platform innovation.

Leads research, design, development, testing, deployment, and sustainment of advanced data engineering solutions using GenAI. Designs data pipelines, structures, databases, and architectures; translates business requirements into technical tasks; leads code reviews and project planning; collaborates across teams; and performs hands-on development using SQL, Python, cloud, containerization, orchestration, and source-control technologies.

Build and operate Grassroots Carbon’s unified data platform, including AWS-based storage, production ingestion pipelines, scheduled datasets, integrations, model deployment, monitoring, security, and access controls. Migrate and reconcile historical PastureMap data while establishing lineage, shared identifiers, validation, and reproducible workflows. Partner with science, product, backend engineering, and operations teams to productionize models and deliver reliable data services supporting soil carbon modeling, grazing management, and rancher-facing products.

Drive architecture and standards for scalable data infrastructure, pipelines, and data management. Build cross-functional data models and reliable batch and streaming workflows, including ingestion of financial and transactional data. Use SQL, Python, cloud data platforms, orchestration tools, and transformation frameworks to ensure data quality, monitoring, compliance, and actionable insights. Lead initiatives, collaborate with stakeholders, and mentor engineers.

Build and operate scalable data infrastructure, including fault-tolerant pipelines, data discovery services, and data lineage systems. Develop backend services that process petabytes of data for machine learning and autonomy teams. Lead system design, code reviews, and technical contributions while using AWS cloud ETL technologies and distributed systems. Collaborate with stakeholders to improve platform adoption and address data customer needs.

Build and own cloud-based data pipelines and governed data products supporting Employee Benefits Sales and Underwriting. Design ELT solutions, integrate complex data, improve data quality, and implement CI/CD and enterprise data engineering practices. Architect hybrid cloud solutions using AWS, Hadoop, Spark, Kafka, Snowflake, and related technologies. Influence architecture and tooling decisions, document technical guidance, mentor teams, evaluate emerging technologies, and contribute to strategic roadmaps.

Lead hands-on development and operation of production data pipelines and platforms using PySpark, Python, SQL, and AWS services. Translate requirements into technical designs, data models, SLAs, and reliable pipeline architectures. Establish engineering, data quality, observability, documentation, and coding standards; conduct code reviews; mentor engineers; manage Agile delivery and releases; and use GenAI tools to improve productivity and solution quality.

Design, build, and optimize scalable AWS and Snowflake data pipelines, databases, data models, and cloud-based solutions. Implement ETL/ELT workflows, data quality and monitoring frameworks, CI/CD pipelines, automated recovery, documentation, and performance improvements. Collaborate with stakeholders and cross-functional teams in an Agile environment while conducting advanced analysis using Python and SQL.

Build and maintain scalable data platforms and systems that transform raw market data into validated analytical datasets, including bars, residuals, curves, volatility surfaces, and greeks. Collaborate with trading, research, and external vendor teams to understand datasets and workflows, implement calculations, document technical solutions, and ensure reliable delivery through strong testing and validation practices.

Develop and maintain advanced data engineering and visual analytics applications using structured and semi-structured data. Design enterprise database strategies, construct large relational databases, establish programming and security standards, integrate systems with existing data warehouses, and improve system performance. The role requires extensive experience with Perl, Python, PL/SQL, PostgreSQL, AWS, and related technologies while supporting sensitive national security missions.

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.