Top Data Engineer Jobs

Supports AI and cloud solution development by extracting, transforming, standardizing, and integrating text, JSON, geospatial, and other datasets. Assists with automated data ingestion, data services, pipelines, and machine learning algorithm integration for testing and operational deployment. Coordinates data requirements with project managers, customers, vendors, software engineers, architects, and data teams while managing changing priorities in a national security environment.

Supports the development and maintenance of data ingestion, transformation, and integration pipelines across enterprise systems. Writes and optimizes SQL, assists with database management, supports BI reporting, monitors data quality and system performance, troubleshoots issues, documents data flows, and gathers requirements for automation. Works under senior engineering guidance while developing skills in cloud platforms, ETL/ELT, data modeling, governance, MuleSoft, and Azure Data Factory.

Designs, develops, and optimizes scalable Azure-based data engineering solutions, including lakehouses, warehouses, ETL/ELT pipelines, Databricks notebooks, and analytics workflows. The role supports enterprise reporting, AI and machine learning initiatives, feature engineering, Generative AI, and MLOps. Responsibilities include performance tuning, CI/CD automation, production support, technical design, lifecycle management, stakeholder collaboration, and translating business requirements into secure, reliable data platforms.

Build, deploy, and operationalize data pipelines and solutions supporting AI, machine learning, and business intelligence. Responsibilities include data transformation, cleansing, governance, security, quality management, source analysis, testing, incident resolution, and defect remediation. The role collaborates across teams, supports data products, educates users, and applies software engineering and DevOps practices to deliver reliable analytical data solutions.

Designs, develops, tests, maintains, and optimizes data pipelines and software systems using SQL, Python, and Apache Airflow. Supports technical projects, documents systems, troubleshoots application performance, evaluates user requirements, and develops technical specifications. Collaborates with epidemiologists, statisticians, and researchers, while mentoring staff and translating academic research needs into software solutions.

Build and operate production-grade AML data products, ETL pipelines, data models, integrations, and ML-ready datasets. Ensure data quality, lineage, security, governance, observability, reliability, and regulatory compliance. Support transaction monitoring, customer segmentation, risk rating, sanctions screening, and financial crime prevention initiatives. Lead architecture decisions, production support, incident response, CI/CD improvements, automated testing, code reviews, mentoring, and engineering standards.

Designs and maintains ELT pipelines, warehouse integrations, data models, and data quality processes. Collaborates with data scientists, analysts, engineers, and stakeholders to deliver scalable analytical solutions. Responsibilities include data integration, governance, security, performance optimization, exploratory analysis, visualization, and cloud-based API ingestion. Requires strong SQL, DBT, Snowflake, Azure Data Factory, cloud, and scripting experience, plus leadership and communication skills.

Design, implement, and optimize large-scale data ingestion, cleansing, transformation, loading, replication, and display systems. Develop data pipelines, profiling, deduplication, matching logic, and custom data-source solutions. Maintain data-layer documentation, collaborate with data scientists on AI/ML workflows, and use Java technologies, REST APIs, Airflow, ETL, SQL, and Linux in a Big Data environment.

The Forward Deployed Data Engineer will manage AI-automated data platform migration projects, ensuring timely delivery and communication with clients while utilizing proprietary tools.

Designs and maintains scalable data pipelines, data quality frameworks, and cloud-native data solutions using Spark, Databricks, Hadoop, AWS, and advanced SQL. Implements validation, monitoring, observability, and automated remediation capabilities. Applies AI/ML, anomaly detection, and statistical modeling to improve data quality. Provides production support, resolves complex incidents, contributes to architecture and engineering standards, and collaborates with cross-functional data and engineering teams.

Lead the design and development of scalable AWS data pipelines, data lakes, warehouses, and migration solutions. Provide technical leadership, mentoring, code reviews, and architectural guidance while partnering with data scientists, analysts, and stakeholders. Establish standards for data quality, security, governance, performance, and scalability. Drive DevOps, CI/CD, and infrastructure-as-code practices, and troubleshoot complex data engineering issues.

Design, build, and maintain batch and streaming data pipelines; ingest, transform, and optimize data for analytics and BI; ensure data quality, reliability, and performance; implement monitoring/alerting; support MLOps and analytics workloads; collaborate with data scientists and BI teams; document AI systems and RAG/Cortex-based solutions.

Develops scalable data and software systems using Python and Apache Spark. Designs APIs, data models, ETL workflows, orchestration, and data interactions while optimizing performance, scalability, integrity, and security. Builds user interfaces, contributes to automated testing, applies Agile practices, and supports CI/CD delivery. The role requires strong software engineering, distributed computing, analytical, and communication skills, with AWS, R, Databricks, and SAS experience preferred.

Designs, builds, and maintains scalable data pipelines and warehouse solutions. Responsibilities include data collection, cleansing, validation, quality assurance, ETL, integrity audits, documentation, anomaly resolution, and technical requirements analysis. The engineer develops SQL-based solutions, Tableau reporting, AWS cloud data infrastructure, and automated testing processes while collaborating with customers and Agile/Scaled Agile teams. The role supports data-intensive research, supervision, regulation, payments, and economic analysis across the Federal Reserve System.

Design and scale reliable data platforms, pipelines, Java microservices, APIs, and reporting infrastructure. Build data validation and reconciliation workflows, support distributed systems, improve pipeline performance and resiliency, and partner with engineering, analytics, and business teams. Contribute to cloud-native infrastructure, CI/CD, Docker, Kubernetes, dashboards, and self-service reporting modernization.

Own Fullbay’s Snowflake data platform end to end, including dimensional modeling, certified metric definitions, semantic layers, identity resolution, transformations, governance, security, cost management, source ingestion, and AI-agent enablement. Build tested SQL and transformation code, establish company-wide data standards, document systems thoroughly, and direct an architecture consultant. Provide guidance for self-service data use while serving as the final authority on definitions, access, and platform design.

Develops and maintains secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets. Processes structured and unstructured data using platforms such as Databricks, Spark, Palantir Foundry, and Maven Smart Systems. Collaborates with data scientists, engineers, and mission stakeholders while applying data validation, modeling, quality, testing, documentation, and Agile practices. The role requires onsite work in a SCIF and support for classified national security missions.

Maintain and troubleshoot Airtable systems, API integrations, dashboards, and automated reporting across financial, moderation, and production data. Validate integrations, resolve data-flow issues, maintain data standards and documentation, and support operational data reliability. The role is fully onsite and provides mentorship from a Senior Data Operations Architect.

Maintain and troubleshoot Airtable systems and API integrations connecting Salesforce, Coupa, AWS finance tools, and operational data sources. Support automated reporting, dashboards, executive performance reports, data validation, documentation, naming standards, and metadata governance. The role focuses on testing, reliability, issue resolution, and onsite support, with mentorship from a Senior Data Operations Architect.

Own and build JSSI’s AI-first data platform, including Bronze, Silver, and Gold pipelines in Microsoft Fabric and OneLake. Develop governed semantic layers for Power BI, ingest data into Azure SQL and ADLS Gen2, and enforce quality, lineage, security, governance, and PII controls aligned with Entra ID. Expose curated datasets to AI agents through approved APIs or MCP servers while using Claude Code and documentation-first engineering practices.

Build and maintain data pipelines and integrations across engineering, manufacturing, procurement, ERP, PLM, and business systems. Develop monitoring, dashboards, automation, and data governance practices that improve operational decision-making. Collaborate with cross-functional teams to translate requirements into durable tools, document system ownership and lineage, and support evolving organizational needs. This is a full-time, onsite role in Denver, Colorado.

Build, optimize, and maintain scalable data pipelines and reusable datasets for business intelligence, marketing, and data science. Develop federated data validation frameworks, optimize queries and data models, and protect user privacy and security. The role uses Python, SQL, AWS, Airflow, PostgreSQL, Iceberg, Snowflake, Spark, and other modern data-stack technologies. It is based in New York City with in-office work three days per week.

Build and maintain a production data platform integrating RMS, Azure SQL, HubSpot, and vendor APIs. Develop pipelines, data models, quality gates, APIs, system-of-record write-backs, performance improvements, and explainable lineage. Build production AI-agent workflows with evaluation, guardrails, and monitoring. Own features end to end, contribute to applications, support incident response, and collaborate with product managers and traders.

Technical lead for Finance Technology data engineering: design and build scalable batch and real-time data pipelines (Hadoop/Spark/PySpark/Scala/Java), optimize data models and APIs, drive cloud-based data architecture (AWS/GCP/Azure), enforce governance/security, and mentor engineers to deliver enterprise financial data services.

Designs and supports scalable data engineering solutions, pipelines, processing systems, and data platforms. Translates business requirements into technical solutions, contributes to architecture discussions, communicates with client stakeholders, and identifies opportunities to apply data and AI technologies. The role is client-facing, onsite or hybrid in Indianapolis, and supports potential engagement expansion.