Top Data Engineer Jobs

Own and scale automated patient-data ingestion and transformation pipelines from EMR systems into clinical-trial matching workflows. Design reliable asynchronous, queue-based, containerized architectures; manage schemas, validation, data quality, monitoring, and production issues. Partner with AI and product engineering teams on healthcare data foundations, APIs, scalability, and trustworthy clinician-facing workflows. Operate autonomously in a startup environment while applying strong systems thinking and pragmatic technical judgment.

Designs and leads scalable real-time and batch data pipelines, data platforms, architectures, data products, and intelligent APIs. Ensures data quality, supports BI and AI/ML initiatives, and develops ingestion, processing, migration, and analytical solutions for big data, NoSQL, and data warehouse environments. Provides technical leadership, mentorship, code reviews, testing, CI/CD implementation, system monitoring, troubleshooting, and cross-functional translation of business requirements into reliable cloud-based solutions.

Design and build modern GCP lakehouse architectures, including Python and Spark pipelines, BigQuery ingestion, Kafka CDC, Delta Lake and Iceberg UniForm integrations, Delta Sharing endpoints, Snowflake and Databricks connectivity, data lineage, governed access controls, and semantic-layer alignment. Collaborate cross-functionally, troubleshoot full-stack issues, and deliver reusable data-sharing adapters and end-to-end data features.

Support SAP BRIM data analysis, migration, and development across SOM, Convergent Charging, Convergent Invoicing, and FI-CA. Design SAP BODS ETL workflows, configure EMIGALL migration objects, develop mappings and reconciliation reports, validate migrated data, troubleshoot issues, and support testing, cutover, production stabilization, documentation, governance, and stakeholder collaboration.

Build and own Snowflake data models and dbt pipelines for a Customer Data Platform supporting marketing segmentation and customer journeys. Integrate complex source systems, manage data architecture and governance, conduct data discovery, and translate business rules into scalable solutions. Collaborate with engineering, analytics, product, and business stakeholders; lead technical workshops; mentor junior engineers; and implement automated data quality testing, CI/CD workflows, and AI-augmented engineering practices.

Build and maintain scalable data infrastructure, pipelines, warehouses, and models on cloud platforms, primarily GCP. Develop batch and real-time ETL/ELT workflows using Python, SQL, and Airflow-based tools; implement data quality, governance, metadata, lineage, CI/CD, and automation. Support production machine learning environments, optimize pipeline performance, collaborate with architects and analytics teams, lead solution delivery, and mentor data engineers.

Designs, builds, optimizes, and tests Snowflake lakehouse architecture and ETL/ELT pipelines integrating Salesforce, marketing, and business data. Develops Bronze, Silver, and Gold data models, semantic layers, and datasets for Power BI, analytics, and AI initiatives. Implements data quality and governance procedures, maintains Git repositories, documents architecture, leads solution design, reviews code, mentors engineers, and communicates technical solutions to stakeholders.

As a Senior Data Engineer at Auger, you will build data systems to transform customer data into a unified ontology for analytics and AI workflows, ensuring data quality and managing the data lifecycle.

Designs, builds, and maintains enterprise lakehouse data pipelines, transformation layers, and data models. Develops scalable batch and streaming solutions using Spark, Kafka, and Flink; implements medallion architecture, data quality monitoring, and infrastructure as code. Troubleshoots performance and data incidents, participates in code reviews, collaborates on data modeling, and mentors junior and mid-level engineers.

Architect, build, and evolve AWS-based data platforms and pipelines handling large, complex datasets. Optimize data solutions for scalability, reliability, performance, and cost efficiency. Collaborate with engineering and product teams, make architectural decisions, establish best practices, provide technical mentorship, and support modernization toward intelligent data architectures. The role involves Python, SQL, Databricks, Kubernetes, automation, DevOps, and agentic AI technologies.

Designs and oversees secure cloud data platforms, dimensional models, lakehouses, scalable ETL pipelines, Spark workflows, and data governance frameworks. Builds sitewide tracking architecture and KPI dashboards, integrates disparate data sources, optimizes query performance, and applies machine learning and AI-assisted development practices. Collaborates with engineering, product, DevOps, and security teams to deliver data solutions. Mentors engineers and analysts and manages projects through completion.

Leads data and analytics engineering for Zero Trust maturity assessment and implementation measurement. Designs common data models, integrates and validates enterprise data, develops maturity and risk metrics, supports prioritization and gap analysis, and establishes data quality, lineage, traceability, and documentation practices. Partners with dashboard and automation specialists to streamline reporting and data refresh workflows.

The Senior Data Engineer will design and develop scalable data architectures, manage ETL processes, optimize databases, and implement MLOps pipelines, promoting data-driven decision-making across the organization.

Own and scale streaming Kafka pipelines and large-scale Spark batch jobs processing billions of rows. Drive pipeline reliability, observability, data modeling, and cost/latency optimizations. Partner with data scientists and platform engineers, define SLOs, improve efficiency, contribute to shared infrastructure (Airflow, libraries, data catalog), and mentor junior engineers.

Own and operate the Snowflake warehouse and Sigma semantic layer; manage ingestion (Fivetran), dbt model scoping and review, reverse-ETL, data quality SLAs, dashboards, month-end financial analytics, vendor management, and automation to support AdOps, Sales, Finance, and executive reporting.

Design, implement, and maintain automated ingestion and transformation workflows to bring spreadsheet-based operational data (JSON/CSV/Excel) into enterprise databases and Pega workflows. Validate data integrity, troubleshoot pipelines, support reporting integrations, document data flows, and collaborate with architects, developers, and stakeholders to modernize and secure enterprise data services.

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.

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.

Build, test, and support batch and streaming data pipelines, curated datasets, and data platform tooling. Responsibilities include data modeling, schema evolution, performance optimization, data quality controls, reconciliation, monitoring, and production support. The role requires collaboration with engineers and data consumers, with opportunities to contribute to technical design, platform standards, and delivery improvements.

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.

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 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.