Top Data Engineer Jobs

Build and lead complex data integration projects from client onboarding through ongoing platform usage. Analyze and resolve data discrepancies, troubleshoot SIS and SSO integrations, develop internal Retool applications, perform code reviews, prioritize backlogs, manage projects, and mentor teammates. The role requires advanced SQL, development expertise, technical leadership, and collaboration with internal stakeholders, with Ruby on Rails, AWS, Git, and K-12 education experience preferred or desired.

Designs, develops, and optimizes enterprise data platforms, ETL workflows, databases, reporting solutions, and dashboards. Leads technical designs, SDLC processes, troubleshooting, documentation, and production support while collaborating with stakeholders and vendors. Uses SQL Server, SSIS, T-SQL, Microsoft BI tools, and DevOps technologies to build scalable, reliable data integration and business intelligence solutions. Supports cloud migration and may use Azure, Fabric, Snowflake, or Databricks.

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, develops, and maintains enterprise databases, ETL workflows, data platforms, reporting systems, and business intelligence solutions. Leads technical design, data integration, automation, troubleshooting, documentation, and production support. Collaborates with stakeholders and vendors to deliver scalable, secure, and reliable data solutions. Requires advanced SQL Server, SSIS, T-SQL, and SDLC expertise, with preferred experience in Microsoft BI tools, APIs, cloud data services, and Python or Java.

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.

Design, build, and operate large-scale Hadoop-based data platforms and pipelines. Develop Spark processing applications, ingest and transform structured and unstructured data, support streaming workloads, and ensure production reliability, performance, and cost efficiency. Work with distributed systems, SQL, relational and NoSQL databases, workflow orchestration, cloud data platforms, lakehouse formats, governance tooling, Kubernetes, and infrastructure automation.

Designs, builds, and operates large-scale Hadoop data pipelines and analytics platforms. Responsibilities include ingesting, transforming, and analyzing structured and unstructured data; developing Spark-based processing solutions; managing streaming workflows; working with relational and NoSQL stores; orchestrating pipelines; troubleshooting production systems; and ensuring reliable, performant, cost-effective data platforms. The role also supports enterprise analytics, machine learning, and reporting workloads.

Architect and lead enterprise data platform strategy across ingestion, storage, processing, governance, security, and consumption. Design scalable lakehouse, warehouse, and streaming architectures; establish modeling, data quality, lineage, and catalog standards; optimize cost, reliability, disaster recovery, and capacity. Partner with ML, BI, product, and engineering teams, lead architecture reviews, mentor technical staff, and deliver architecture documentation and data product principles.

Design, build, and operate large-scale Hadoop-based data pipelines and analytics platforms. Responsibilities include ingesting, transforming, and analyzing structured and unstructured data; developing Spark applications; supporting streaming workloads; managing distributed systems; troubleshooting production environments; and delivering reliable, performant, cost-effective data platforms for analytics, machine learning, and reporting.

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.

Design, build, and optimize scalable data platforms, pipelines, and lakehouse/warehouse solutions using Microsoft Fabric and Azure. Implement ETL/ELT, data migration from SQL Server, governance, CI/CD, streaming integrations, and Power BI connectivity to enable analytics and AI initiatives while ensuring security and performance.

Designs, builds, and optimizes data pipelines and architectures (ETL/ELT) to support analytics and ML teams. Assembles complex datasets, implements process improvements, collaborates with IT on AWS and Snowflake infrastructure, applies statistical techniques, supports reporting and analytics, and ensures compliance with financial regulations.

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.

Lead technical design and implementation of production-grade data platforms, ETL/ELT and streaming pipelines, containerized Python data applications, Snowflake and dbt modeling, observability, CI/CD, and AI/ML data integrations to enable scalable data products and business-aligned analytics.

Lead design and build of Snowflake-native medallion data architecture, ETL/ELT pipelines, master data and governance, and an enterprise semantic layer. Partner with Finance to produce reliable financial data products, optimize performance/cost, enforce data quality and compliance, mentor junior engineers, and support AI use cases and observability.

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.

Lead design, maintenance, and optimization of on-prem and cloud data platforms. Perform DBA duties for SQL Server, manage HA/DR and replication, troubleshoot performance, lead on-prem to Azure migrations, maintain Azure data services and Microsoft Fabric, optimize cloud cost/performance, implement monitoring, collaborate with architects and BI teams, and mentor junior engineers.

Provide day-to-day administration and production support for Snowflake, Matillion, and related data platforms. Monitor jobs and performance, troubleshoot ETL/ELT and report refresh failures, manage access and deployments, maintain runbooks, and build AI-driven automation and agent-based workflows to improve operational reliability and efficiency.