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Design, build, and operate scalable batch and streaming data pipelines and analytical data models. Lead data architecture across ingestion, transformation, storage, and serving layers while implementing testing, CI/CD, observability, quality, lineage, governance, and access controls. Integrate restaurant and enterprise data sources, optimize platform performance and cost, deliver production-ready datasets for BI and AI, mentor engineers, and collaborate with cross-functional technical stakeholders.

The Staff Data Reliability Engineer ensures data integrity, quality, freshness, and availability across enterprise pipelines. Responsibilities include defining data SLOs, implementing observability, building resilient Informatica and Python/PySpark pipelines, automating validation and recovery, eliminating operational toil, leading data incidents and postmortems, creating runbooks, and applying AIOps, machine learning, and DataOps practices to detect anomalies and self-heal data workflows.

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.

Leads enterprise data engineering initiatives by designing and optimizing data pipelines, data lakes, warehouses, and analytics infrastructure. Uses Snowflake, Informatica, SQL, and related technologies to integrate diverse data sources, improve data quality, secure PII, and support analytics and data science teams. Establishes governance, monitoring, scalability, performance, and cost-efficiency practices while mentoring teammates and partnering with stakeholders on complex technical solutions.

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 Power BI and MicroStrategy dashboards and reports; perform ETL and data integration from diverse sources; support IT operations through data analysis; ensure data accuracy and compliance with DoW STIGs and IL5/IL6/IL7; document solutions and train users; collaborate with stakeholders to translate requirements into visualizations.

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.

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.

Analyze critical stack-component data for yield, performance, and capacity improvements. Develop engineering software tools and image-processing algorithms for characterization and metrology, collaborate cross-functionally on yield and quality improvements, and evaluate software and hardware for new component characterization metrics.

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.