Top Senior Data Engineer Jobs

Designs and delivers scalable data engineering, analytics, automation, AI-enabled solutions, and supply chain applications. Builds Python solutions, Snowflake data pipelines, data models, reporting, and self-service analytics supporting planning, procurement, logistics, forecasting, inventory optimization, and operational decision-making. Partners with business and technical teams to translate requirements into reliable solutions, improve processes, maintain data quality, and provide technical leadership across analytics and data engineering initiatives.

Designs, develops, and operates production data pipelines and curated, AI-ready data products for AbbVie R&D. Responsibilities include data integration, normalization, modeling, metadata, lineage, quality controls, governance, documentation, knowledge graph publication, and support for RAG and vector-search workflows. The role collaborates with scientists, engineers, architects, data owners, and vendors; provides technical guidance; monitors pipeline reliability; resolves issues; and ensures compliance with data security, GxP, and regulatory standards.

Designs, builds, deploys, and maintains scalable cloud-native data platforms and pipelines in AWS. Supports data acquisition, preparation, database architecture, cloud migration, AI/ML, data science, and business intelligence initiatives. Leads collaborative teams in evaluating data, designing solutions, and managing operational data flows. Requires TS/SCI clearance with polygraph and experience in all-source analysis and data engineering.

Designs, builds, deploys, and maintains scalable cloud-native data platforms and pipelines using AWS. Supports cloud migration, data science, AI/ML, and business intelligence initiatives by organizing structured and unstructured data. Responsibilities include database architecture, data acquisition and preparation, software development, platform modernization, analytics architecture evaluation, and cross-functional team leadership. Requires TS/SCI clearance with polygraph and substantial all-source analysis experience.

Designs and develops cloud data architectures, ingestion frameworks, data models, and analytical platforms. Advises clients on cloud migration, data warehousing, technical approaches, and implementation roadmaps. Collaborates with stakeholders through requirements gathering, workshops, whiteboarding, and project meetings while deploying end-to-end cloud analytics solutions. Requires experience with cloud platforms, big data technologies, BI tools, data warehousing methodologies, programming, SQL, version control, and Agile/DevOps practices.

Own the technical direction, architecture, roadmap, and operational health of Teamfight Tactics’ data ecosystem. Build scalable batch and near-real-time data systems, telemetry, schemas, data models, governance, observability, and reliable sources of truth. Lead cross-functional initiatives, define engineering standards, optimize costs, mentor practitioners, and partner with game, product, insights, and data science teams across feature development and live operations.

Designs, builds, and optimizes cloud-native data platforms, pipelines, data models, and integration frameworks. Develops scalable ingestion and transformation solutions using PostgreSQL, Python, SQL, AWS services, and Apache Airflow. Establishes data engineering standards, monitoring, governance, observability, and automated recovery. Collaborates with business, architecture, analytics, and Agile teams to deliver secure, reliable data products and reporting capabilities.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Build and maintain recruiting analytics data infrastructure using BigQuery, dbt, Fivetran, SQL, and Python. Design dimensional models, ETL/ELT pipelines, semantic layers, governance controls, data security, monitoring, and standardized recruiting metrics. Integrate Workday, Greenhouse, and internal systems while supporting batch and real-time analytics. Partner with data scientists, engineers, recruiters, and business stakeholders to deliver trusted data products and AI-enabled workforce insights.

Design and scale lakehouse architecture, streaming and batch pipelines, and reliable data platforms using Kafka, Spark, Airflow, Iceberg, Databricks, and related technologies. Build Medallion-layer data systems, manage open table formats, optimize distributed queries, monitor platform reliability, resolve data quality issues, and collaborate with analysts, data scientists, and product teams.

Build and manage data ingestion, integration, transformation, modeling, and quality frameworks for federal financial management and IT programs. Analyze large datasets using SQL, Python, R, PySpark, and Databricks; develop predictive models and apply machine learning techniques. Ensure data governance and quality standards, define Databricks data quality metrics, support client meetings and documentation, and contribute to consulting and business development activities.

Build, deploy, and maintain scalable data pipelines and platforms for mission-driven projects. Validate and normalize datasets, develop schemas, implement ETL workflows, and create monitoring dashboards and metrics. Collaborate with AI/ML engineers, cloud architects, and technical leaders to support governed, high-quality data flows and reliable model performance. Improve pipeline performance and support data engineering for operational, research, and enterprise systems.

Designs and operates secure batch and streaming data pipelines across cloud platforms. Responsibilities include data ingestion, integration, modeling, quality validation, governance, lineage, security, monitoring, workflow orchestration, and performance optimization. The role supports analytics, machine learning, AI-enabled applications, and authoritative data services while documenting architectures, runbooks, and data contracts. Collaboration with engineering, cloud, security, data science, architecture, and customer teams is required, along with contributions to secure data platform proposals.

Lead the design, development, optimization, and monitoring of scalable cloud data pipelines using AWS Glue, PySpark, and Amazon Redshift. Integrate diverse data sources, ensure data quality, deploy infrastructure with Terraform, and improve reliability, performance, and cost efficiency. Collaborate with data professionals and stakeholders, document architecture standards, ensure security compliance, mentor junior engineers, conduct code reviews, and evaluate AWS technologies. The role requires a bachelor’s degree, substantial cloud data engineering experience, and hybrid work in specified locations.

Design and optimize Snowflake data warehouses, dbt transformation models, and scalable ETL/ELT pipelines. Develop ingestion utilities, improve SQL and warehouse performance, establish data quality and governance controls, and maintain CI/CD workflows using Git. Collaborate with analysts, data scientists, and business stakeholders across manufacturing, supply chain, and operations. The role also supports modern DataOps practices and may involve orchestration tools and cloud platforms.

Lead design, development, and operation of large-scale Spark-based data pipelines and lakehouse solutions. Write and review Python/PySpark and Spark SQL production code, optimize Spark workloads on EMR/Databricks, manage Apache Iceberg table operations, drive AI-assisted engineering adoption, automate reliability and CI/CD practices, and lead technical evaluations and communities of practice.

Designs, develops, and maintains scalable AWS data pipelines and infrastructure. Responsibilities include data ingestion, ETL transformation, storage, orchestration, monitoring, optimization, troubleshooting, data quality, governance, security, and privacy. The role partners with stakeholders to develop data solutions and supports reliable, accessible data for decision-making.

Design and deliver enterprise-scale data models and analytics-ready datasets in Snowflake; build reliable ELT/ETL pipelines (batch and near-real-time); enforce data quality, observability, and secure handling of PII; optimize performance and publish curated data to downstream systems while collaborating with product, operations, and engineering teams.

Designs, builds, and maintains cloud data pipelines and ETL/ELT processes supporting VA healthcare analytics. Responsibilities include patient matching, record linkage, data harmonization, quality management, documentation, performance optimization, and pipeline support. Collaborates with analysts and stakeholders to develop scalable solutions and Power BI reports using Databricks SparkSQL. The role requires federal healthcare data experience, strong SQL or Spark SQL skills, and the ability to obtain a VA Public Trust clearance.

Lead the modernization of a healthcare data platform from an MSSQL XML object store to a scalable AWS architecture. Evaluate AWS storage and query services, design SQL reporting layers, architect data migration and metadata strategies, optimize performance and cost, and build CI/CD and infrastructure-as-code pipelines. Partner with business stakeholders, analysts, and C#/.NET engineers to deliver accessible data models and reliable integrations. Document architecture, data flows, and migration processes while supporting healthcare data and compliance requirements.

Designs and leads enterprise-scale Snowflake data engineering solutions, including data warehouses, ELT pipelines, data models, cloud integrations, orchestration, and performance optimization. Builds scalable batch and streaming pipelines, APIs, data quality controls, automated tests, and reusable engineering components. Partners with architecture, security, governance, DevSecOps, and business stakeholders while supporting data analysis, application design, coding, deployment, and production maintenance.

Design, build, and maintain scalable data pipelines, warehouses/lakes, and platforms. Lead multidisciplinary teams to implement ETL/ELT (batch and streaming), optimize data lake performance, enforce data governance, and support analytics and ML use cases on cloud and on-prem environments.

Design, build, and maintain reliable ETL/ELT pipelines, data models, warehouses, lakes, and databases. Implement data quality, monitoring, cleansing, and security practices while supporting AI-ready data capabilities and analytics initiatives. Collaborate with architects, analysts, business stakeholders, vendors, and consultants to translate requirements into scalable technical solutions. Document processes, troubleshoot issues, optimize workflows, and manage multiple projects in a deadline-driven environment.

Design, build, and scale TaxRise’s data infrastructure on AWS using Databricks. Own end-to-end pipelines, ETL/ELT processes, data modeling, orchestration, governance, cluster optimization, and delivery for reporting and analytics. Collaborate with the Chief Data Officer, Salesforce developers, data engineers, and full-stack developers to establish platform standards and support business growth.

Designs and maintains scalable data architectures, ETL workflows, batch and real-time pipelines, and SQL/NoSQL storage for intelligence operations. Ensures data quality, governance, security, compliance, documentation, and lineage across multi-INT datasets. Collaborates with analysts and technical teams to translate intelligence requirements into secure solutions and participates in mission planning and coordination forums.