Top Data Engineer Jobs

Own and scale Provable's data platform on GCP: build and optimize ingestion pipelines from blockchain indexers, design privacy-aware data models, manage Postgres and BigQuery, develop high-performance data-serving APIs, ensure data quality and observability, and use IaC (Terraform, Helm) and Kubernetes to deploy and cost-optimize environments.

Design, build, and maintain scalable ETL/ELT data pipelines and data platform architecture. Build programmatic pipelines (primarily Python), optimize analytical data models (SQL), integrate sources into Snowflake, use orchestration/transformation tools (dbt, Airflow, Dagster), improve data quality/observability, and partner with product, engineering, and analytics to deliver reliable data for decision-making.

Design, develop, test, and maintain ETL solutions and data warehouse structures using SSIS and SQL Server. Build and support SSAS models, optimize ETL performance, ensure data quality, partner with stakeholders, and produce technical documentation to support BI and reporting initiatives.

Design, build, and operate scalable ingestion, curation, and metadata-enrichment pipelines for enterprise data catalog and discovery. Ensure data quality, schema evolution, lineage, and cost-efficient large-scale processing; translate technical concepts for non-technical stakeholders and support downstream mission use.

Design and maintain cloud-native data platforms (lakes, lakehouses, warehouses) on AWS/Azure/GCP. Build CI/CD templates and IaC for self-service, develop ETL/ELT for batch and streaming, enforce RBAC, encryption, data lineage and compliance, monitor performance and cost, and collaborate with architects, developers, and data scientists to integrate APIs and deliver end-to-end data products.

Lead architecture and execution of Faire's data platform and analytics capabilities. Build and operate scalable, secure ETL/ELT, streaming, storage, compute, orchestration, and data warehousing at petabyte scale. Drive performance, cost, monitoring, governance, and self-serve analytics; influence cross-functional teams and mentor engineers.

Design and maintain a dbt-based business data layer for GTM metrics, centralize and reconcile multi-source data, implement automated data quality and reconciliation checks, align and document metrics with stakeholders, mentor analytics team, and lead root-cause investigations into complex data anomalies.

Design and build production-grade data pipelines on Databricks and AWS extracting from enterprise ERPs, implement medallion architectures, ensure data quality/governance, collaborate with business SMEs, and deliver AI-ready data products.

Leads enterprise data ingestion and integration for AI, machine learning, Generative AI, analytics, and BI workloads. Designs scalable pipelines for structured, semi-structured, unstructured, and multimodal data; establishes quality, governance, lineage, security, and observability standards; and supports RAG, AI Search, vector retrieval, and MCP capabilities. Partners with technical and business stakeholders, mentors engineers, evaluates emerging technologies, and drives production delivery and continuous improvement in a regulated financial-services environment.

Develop and support Informatica MDM and IDMC solutions across customer, supplier, product, material, and finance domains. Design master data configurations, integration pipelines, data quality rules, mappings, and governance workflows. Integrate MDM with SAP S/4HANA, ERP, cloud, and legacy systems while supporting cloud modernization, production troubleshooting, deployments, testing, and CI/CD. Collaborate with data governance teams, business stakeholders, and Agile engineering teams to deliver scalable, accurate, and compliant enterprise data solutions.

Designs and implements Oracle Cloud data migration solutions by analyzing legacy sources, mapping fields, developing ETL processes, validating transfers, troubleshooting conversion issues, and documenting procedures. Collaborates with business and technical teams, supports testing and post-implementation activities, and helps optimize large-scale, multi-system data conversion initiatives.

Designs and operates scalable serverless AWS data pipelines, data models, ETL/ELT workflows, feature stores, and analytics-ready data assets. Partners with AI, Analytics, Product, and Underwriting teams to support reproducible ML training and scoring, MLOps, data quality, monitoring, governance, and production troubleshooting. The role handles sensitive healthcare and financial data, optimizes serverless workloads, supports batch and near-real-time processing, and applies infrastructure-as-code, CI/CD, documentation, and responsible data-use practices.

Lead the architecture and implementation of scalable batch and streaming data pipelines across GCP, AWS, and Azure. Build production-grade Python data products, AI/ML, RAG, vector database, and agentic workflows; establish MLOps, LLMOps, CI/CD, containerization, observability, governance, and security practices. Optimize platform performance and cloud costs, resolve incidents, translate business strategy into technical solutions, set engineering standards, and mentor data engineers through hands-on coding.

Own and operate Priority Waste’s enterprise data platform, building Azure and Microsoft Fabric pipelines, warehouse models, Power BI semantic models and dashboards. Integrate operational, payroll, fleet, telematics, API and file-based data sources. Manage monitoring, alerting, data quality and governance while expanding AI-driven reporting and reconciliation automations. This hands-on, on-site role requires independently scoping, building, deploying and operating production data systems as the company’s primary data engineer.

Designs, builds, and optimizes batch and real-time data pipelines, warehouses, streaming solutions, analytical data models, and containerized data services. The role integrates diverse data sources, automates workflows, deploys secure multi-cloud services, embeds security scanning and encryption into CI/CD pipelines, and collaborates in Agile teams while mentoring junior engineers. Required expertise includes Spark, Kafka, Airflow, cloud data platforms, Python or related languages, databases, data modeling, Docker, and Kubernetes.

Provides technical leadership for enterprise data platform engineering. Designs and implements ETL, ELT, streaming ingestion, data models, cloud data lakes and warehouses, monitoring, data quality, governance, testing, version control, and CI/CD practices. Leads AWS, Snowflake, and dbt implementations while integrating disparate data sources for analysts and data scientists. Partners with business stakeholders to align data products with organizational strategy and enables self-service analytics and automation.

Lead the design, development, operation, and optimization of enterprise-scale Azure data platforms. Build scalable pipelines, manage data loads, improve performance and storage costs, ensure data quality and reconciliation, resolve production issues, and strengthen governance and security. Collaborate with architects, engineers, analysts, and business stakeholders while championing engineering standards, DevOps, continuous improvement, and knowledge sharing.

The Senior Data Engineer designs and optimizes data pipelines for marketing analytics, ensuring data accuracy and collaborating with teams to meet reporting needs.

Designs, develops, optimizes, and supports an enterprise Databricks and AWS data platform. Builds scalable PySpark and Databricks SQL pipelines, Delta Lake medallion architectures, data models, integrations, and CI/CD workflows. Supports AWS infrastructure, governance, security, monitoring, testing, deployments, incident response, and production stability. Collaborates with product, project management, QA, DevOps, and external partners to deliver reliable data solutions for analytics and AI/ML use cases.

Owns end-to-end data engineering on Microsoft Fabric, including Bronze, Silver, and Gold medallion pipelines, event-driven ingestion, CDC, schema evolution, data quality, orchestration, and production observability. Manages Azure DevOps CI/CD and environment promotion, integrates data feeds with Pecan AI models, and supports Power BI semantic models and governance. Requires advanced SQL, Python, PySpark, Delta Lake, Azure, KQL, Power BI, Git, and data engineering experience.

Lead the design, development, and optimization of scalable batch and real-time data pipelines and platforms. Build cloud-based data solutions using distributed processing, streaming technologies, and modern data warehouses. Define data models, improve performance and reliability, establish engineering best practices, and provide technical guidance to data engineers while collaborating with architects, product teams, developers, and stakeholders.

Design and implement scalable data pipelines, support data ingestion and quality, collaborate on data models, and maintain data exports, contributing to technology improvements.

Design and maintain healthcare data integration pipelines, models, and validation processes for claims, billing, clinical, pharmacy, EMR, FHIR, and value-based care data. Collaborate with clients and internal analytics, product, and customer success teams to interpret data, document business rules, support implementations, and build reusable Ursa data models. Apply healthcare domain expertise, data security practices, HIPAA compliance, and appropriate judgment when using AI-assisted tools.

Develop and maintain big data solutions using Hadoop, Spark and Scala. Work with Kafka and Cassandra for data streaming and storage. Require hands-on experience on large projects, strong debugging skills, and log analysis to diagnose production issues.

Design, build, and support enterprise data platforms for analytics, reporting, and regulatory needs. Develop and optimize database objects and data models, automate data workflows with Python/Perl/Shell, provide Level 3 production support, improve data quality and performance, and leverage AI/LLM tools to enhance automation and analytics while embedding risk and control practices.