Top Data Engineer Jobs

Own and evolve Later’s data platform across ingestion, orchestration, storage, transformation, and consumption. Build reliable batch and real-time pipelines, backend services, APIs, infrastructure as code, CI/CD automation, observability, data quality controls, and secure GCP-AWS data movement. Modernize BigQuery and lakehouse infrastructure, improve performance and cost, mentor engineers, and establish governance, metadata, lineage, and freshness foundations for analytics, machine learning, LLMs, and AI agents.

Owns the architecture and development of pricing data pipelines, models, platforms, and automation. The role supports price waterfall analysis, discount optimization, segmentation, margin analytics, data quality, near-real-time insights, and machine learning use cases. Responsibilities include production Python development, SQL-based ETL/ELT, cloud pipeline optimization, testing, monitoring, source control, technical mentoring, and collaboration with pricing, commercial, and technical stakeholders.

The Senior Data Engineer designs and maintains production data pipelines on GCP using BigQuery, Dataform, Airflow, Dataflow, and Cloud Run. They own data validation, cleansing, troubleshooting, API integrations, and reliable datasets for analytics and AI/ML. The role also involves translating client requirements into data solutions, maintaining dashboards, supporting ad hoc requests, and applying Git, code reviews, and CI/CD practices.

Build and operate reliable ETL and ELT pipelines across lakehouse and transactional systems. Develop data models, enforce governance, integrate diverse data sources, and support analytics, APIs, and predictive tools. Monitor performance, data quality, lineage, cost, and uptime while collaborating with data science, AI, product, and innovation teams.

Supports the Fraud Data Engineering team through data integration, quality validation, analytics, documentation, and platform modernization. Responsibilities include gathering requirements, creating business requirement documents and source-to-target mappings, enabling batch and real-time integrations, analyzing large datasets, documenting data flows, participating in Agile activities, and developing Databricks notebooks and validation frameworks. The role is a 12-week internship focused on fraud and security data solutions.

Supports enterprise data engineering initiatives involving real-time event streaming, Voice of the Member analytics, master data management, data quality, metadata, and AI technology proofs of concept. The intern analyzes data flows and feedback, assists with platform governance and automation, researches technology trends, collaborates with engineers and stakeholders, and presents findings. The 12-week program requires current enrollment in a technical degree program with graduation in December 2027 or later.

Own and maintain dbt data models across design, testing, deployment, monitoring, and documentation. Translate ambiguous Marketing and Product needs into reliable data solutions, write design documents, support production issues, create runbooks and schema documentation, and coach junior engineers. The role uses SQL, Python, Snowflake, dbt, Dagster, and AWS while partnering closely with Marketing, Data, and MarTech teams.

Build and govern Anthropic’s quote-to-cash data foundation. Design canonical models from Salesforce, CPQ, and billing data; establish data integrity standards and SLAs; collaborate with systems engineers on schema changes and ingestion; and create data products, dashboards, and self-service analytics tools for GTM and Finance teams. The role also owns data architecture, translates stakeholder needs into technical requirements, and influences roadmaps.

Leads the design and lifecycle management of Oncology R&D data products, including data modeling, architecture, ETL/ELT pipelines, data integration, quality controls, governance, and AI-ready datasets. Partners with scientific, clinical, data science, and IT stakeholders to translate requirements into scalable solutions across clinical, preclinical, real-world, genomic, and omics data. Supports advanced analytics, ML, and GenAI use cases while ensuring interoperability, reliability, compliance, and operational performance.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Build and operate scalable healthcare data pipelines, dbt models, event-driven services, APIs, and Google Cloud infrastructure. Model and govern multimodal EHR, genomic, and imaging data for AI agents across federated hospital networks. Ensure data quality, lineage, observability, security, HIPAA compliance, and production reliability while contributing to TypeScript application services and agentic workflows.

Build and maintain automated ETL/ELT data pipelines, improve data quality and monitoring, optimize Postgres and BigQuery schemas and queries, develop internal tooling, support data governance (HIPAA), participate in on-call rotation, and collaborate across engineering teams to scale data systems.

Leads customer-facing enterprise data engineering engagements for federal clients, designing scalable data architectures, pipelines, integrations, APIs, object storage solutions, and Java-based applications. Responsibilities include architecture workshops, technical implementation, troubleshooting, documentation, customer communication, mentoring junior engineers, and service innovation. The role requires active Top Secret clearance, at least eight years of relevant experience, strong consulting skills, and expertise across enterprise software, data engineering, and integration technologies.

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

Designs, builds, and maintains enterprise data pipelines, ETL processes, integrations, cloud data infrastructure, and complex data models. Supports analytics partners, develops data warehouse solutions, tunes SQL and database performance, troubleshoots production issues, and delivers end-to-end data engineering projects. The role collaborates with architects, analysts, developers, and business stakeholders, applies big data technologies, supports agile delivery, and mentors team members.

Build and operate production-grade data pipelines and products for manufacturing customers. Lead discovery, architecture, implementation, testing, deployment, documentation, and operational handoff. Model manufacturing data, ensure data quality and observability, and create reusable pipelines and analytics products. Work directly with customer data, manufacturing, IT, product, and engineering teams to solve ambiguous operational problems and deliver reliable solutions.

Lead the architecture and technical direction of Curaleaf’s modern data and analytics ecosystem. Design scalable Snowflake platforms, data models, ingestion and transformation pipelines, governance practices, CI/CD workflows, and reusable data products. Partner across engineering, analytics, data science, governance, and business teams; mentor engineers; optimize performance, security, cost, reliability, and workload management; and communicate technical tradeoffs to executives and stakeholders.

Provides data engineering and analysis consulting for a Department of Defense client. Responsibilities include data modeling, data integration, software development, collaboration with clients and technical teams, and delivery of data solutions. The role requires an active Secret clearance, a bachelor’s degree, and at least three years of relevant experience. Preferred qualifications include Palantir, visualization, big data, programming, ETL, consulting, and Agile experience.

Designs, builds, and maintains data infrastructure, ETL pipelines, data models, and analytics data warehouses. Ensures accurate data flow from creation through presentation layers and supports reporting, analytics, and machine learning initiatives. Collaborates with stakeholders to deliver data products, improves engineering tooling and pipelines, participates in on-call support, and uses cloud, containerization, orchestration, streaming, and CI/CD technologies.

Design and lead development of TrendAI’s large-scale data platform, including ingestion, CDC, replication, transformation, governance, cataloging, semantic discovery, and analytics capabilities. Enable governed self-service dbt across OLTP systems, build distributed SQL and streaming architectures, establish data quality and lineage standards, and create reusable platform APIs. Provide technical leadership, mentor engineers, and guide cross-team architecture for a security-focused, multi-product data platform supporting analytics and AI applications.

Leads the design, development, and optimization of scalable batch and streaming data pipelines for drilling, equipment, and operational analytics. Builds data models and features supporting reporting, machine learning, AI, predictive maintenance, and drilling optimization. Ensures data quality, testing, monitoring, observability, and CI/CD reliability while providing technical leadership, mentoring, and cross-functional collaboration with engineering, data science, and operations teams.

Own the technical direction and roadmap for Faire’s data infrastructure. Build CDC and streaming ingestion from production databases into Kafka, Iceberg, and S3; manage data quality, contracts, observability, SLAs, and reliability. Operate Airflow and Fivetran, lead platform migrations, establish governance, mentor engineers, and collaborate with data scientists, analysts, and senior engineering teams. The role requires hands-on development, architecture leadership, incident ownership, and cross-team influence.

Designs, develops, and optimizes data warehouses, ETL/ELT workflows, data models, and reporting infrastructure. Ensures data quality, performance, governance, and reliability across systems. Collaborates with analysts and data scientists, participates in code reviews, monitors cloud-hosted environments and data pipelines, automates routine tasks, and troubleshoots application and service issues. The role also evaluates data and infrastructure technologies to support scalable analytics and business intelligence solutions.

Owns the analytics layer between curated enterprise data and business users. Builds governed Gold-layer data products, dimensional models, Power BI semantic models, standardized metrics, and reusable data assets. Implements Unity Catalog governance, metadata, lineage, security, quality monitoring, reconciliation, and deployment controls. Partners with analysts, business owners, IT, data stewards, and AI engineers to deliver trusted, documented data for self-service analytics and AI consumption.

Develop and maintain ETL pipelines supporting data ingestion, transformation, preparation, labeling, querying, and analytics. Troubleshoot data and system issues at scale, work with structured and unstructured data, and support government and cybersecurity data operations. Collaborate within an agile team using Linux, cloud, Kubernetes, and data-flow technologies while ensuring quality, testing, and timely delivery.