Top Data Engineer Jobs

Lead the architecture, development, and maintenance of scalable data pipelines and platform infrastructure supporting satellite operations, analytics, machine learning, and business decisions. Build data warehouses, lakehouse systems, data models, and dbt transformations; operate platforms using Databricks and related cloud services; and ensure data quality, reliability, security, governance, observability, lineage, and cost efficiency. Collaborate with product owners and multidisciplinary engineering teams on data requirements and platform capabilities.

Build and lead Harvey’s central data platform, including streaming and batch ingestion, CDC, Snowflake warehousing, orchestration, transformations, real-time processing, and self-service tooling. Establish data quality, observability, lineage, governance, PII protection, access controls, and multi-region data residency. Own architecture and technical direction while setting standards, mentoring engineers, and partnering with Analytics, Data Engineering, Product, and Infrastructure teams.

Build and lead Harvey’s central data platform, including streaming and batch ingestion, CDC, Snowflake warehousing, orchestration, transformation frameworks, real-time processing, self-service tooling, and data quality, lineage, and governance. Establish secure patterns for PII, access control, retention, and multi-region data residency while setting technical direction and mentoring a growing team.

Designs, develops, and maintains Databricks-based ETL pipelines, data lake architectures, datasets, and data quality processes supporting reporting, analytics, and AI/ML workloads. Maintains and migrates legacy SSIS and SQL Server systems, optimizes pipeline performance, and supports complex SSRS reporting. Collaborates with business users, analysts, and IT teams to define data requirements, troubleshoot issues, document solutions, and advance AI-assisted engineering practices.

Designs and maintains data models, databases, dictionaries, ETL workflows, and data quality processes. Partners with data owners and reporting teams to define requirements, resolve discrepancies, establish stewardship, and improve data sharing. Develops scalable PySpark and Python pipelines, optimizes SQL and Spark jobs, and implements AI/ML solutions using technologies including Snowflake and Oracle. Requires collaboration across teams, strong documentation, and adherence to data standards.

Designs, develops, and maintains Databricks ETL pipelines, data lake architectures, datasets, and data integrations supporting reporting, analytics, and AI/ML workloads. Troubleshoots and optimizes Databricks and SSIS processes, maintains legacy systems, implements data quality controls, and supports data warehousing. Maintains SSRS reports and collaborates with business users, analysts, and IT teams to deliver reporting solutions. Documents work, communicates technical information, and contributes to AI-assisted development practices.

Build scalable data pipelines, ELT processes, infrastructure, and dimensional data models supporting internal analytics and customer-facing data products. Implement data quality monitoring, auditing, alerting, warehouse optimization, and governed semantic layers for AI and LLM-enabled analytics. Partner with technical and business stakeholders, improve data processes using AI, and deliver reliable metrics from diverse data sources.

Designs and owns production data schemas and models, builds AWS data infrastructure and artifact storage, and develops edge-to-cloud pipelines for robotic systems. The role also delivers ML infrastructure for training pipelines, dataset versioning, and model deployment. Candidates must have at least two years of AWS data infrastructure experience, strong distributed-systems modeling skills, ML infrastructure experience, and the ability to work across the stack. Travel to customer sites is required. Robotics, autonomy, industrial IoT, edge computing, and manufacturing experience are preferred.

Designs and builds scalable, reliable, observable batch and streaming data pipelines and models. Partners with stakeholders on data integration, validates data quality, writes tests, participates in code reviews, documents solutions, troubleshoots production issues, and aligns technical work with business needs. The role requires strong programming, database, data modeling, governance, and Databricks knowledge, with preferred experience in Spark, AWS data services, CI/CD, testing, orchestration, and transformation frameworks.

Own and scale the data platform powering ReadyOn's real-time labor OS. Design and operate batch and real-time pipelines, transformations, and data services (Python, PySpark, Airflow, Glue, Snowflake, Postgres). Build connectors, observability, data quality, and feature-store infrastructure while partnering with AI, backend, and product teams and providing technical leadership and mentorship.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Design, build, and maintain scalable ETL pipelines, data warehouses, and analytics infrastructure integrating EMR, revenue cycle, and operational data. Ensure data quality, HIPAA compliance, support reporting/BI, enable analytics, and collaborate with clinical, finance, and IT teams.

Design, build, and maintain cloud-based data pipelines and products to support analytics and ML. Create discoverable datasets, collaborate with embedded teams to capture system metrics, perform aggregation and visualization, develop production-ready Python/SQL/PySpark workflows, and implement monitoring, dashboards, and alerting to ensure data quality and system health.

Lead enablement and coordination across domain teams to establish enterprise cloud data standards, platform onboarding, and data warehouse integration. Drive adoption of data engineering frameworks, reusable components, governance, data quality, stakeholder alignment, and continuous improvement to scale platform delivery across multiple teams.

Designs, builds, and operates high-throughput streaming and batch data infrastructure for AI products. Responsibilities include developing Kafka, Flink, and Airflow pipelines; managing analytical data layers; optimizing query and pipeline performance; implementing Terraform and GitOps practices; improving observability, reliability, and cost efficiency; partnering with product and research teams; and participating in on-call operations and automation initiatives.

Design, build, deploy, and operationalize scalable data engineering solutions for customers. Responsibilities include developing pipelines, transforming and cleansing data, analyzing sources, implementing governance, security, and quality practices, testing data movement and transformation code, resolving defects and incidents, and advising customers on technical decisions. The role also supports AI, machine learning, business intelligence, and data product adoption while collaborating across teams and educating end users.

Designs and develops enterprise cloud data solutions, including batch, near-real-time, and API-based pipelines. Builds data models, warehouses, lakes, and lakehouses while implementing governance, quality, lineage, and curation practices. Supports CI/CD, testing, monitoring, observability, security, and compliance. Collaborates with architects, product owners, analysts, and leadership on platform strategy and modernization. Mentors engineers, leads reviews, presents technical topics, and explores AI-enabled data capabilities.

Lead modernization of data platforms, design and maintain ETL/ELT pipelines and data warehouse models, improve data governance and quality, optimize platform performance, and collaborate with IT, business units, and leadership to drive data-driven initiatives and stakeholder alignment.

Design, build, and maintain cloud data pipelines and governance on Databricks or Snowflake. Optimize Databricks clusters, implement Delta Lake, automate CI/CD with Azure DevOps, integrate with Azure services, and create monitoring and governance solutions.

Design, implement, and maintain Azure-based ELT/ETL pipelines and data architecture. Migrate and normalize source data into ADLS, enforce quality controls, source control, documentation, and self-service analytics. Optimize storage/processing, audit pipelines, and explore AI-assisted tooling.

Lead design and operation of a cloud-native data platform: set technical direction, build scalable ingestion/processing/warehousing pipelines (batch & streaming), support ML data needs, mentor engineers, drive projects end-to-end, run planning/standups/on-call, and eventually lead a data engineering team.

Own and scale the data platform: design and implement ETL/ELT pipelines, optimize warehouse/lake architecture, support streaming and batch ingestion, enable ML workflows, mentor engineers, write design documents, participate in sprints and on-call rotation, and deliver reliable clinical data at scale to customers.

Design, build, and maintain scalable data pipelines and architectures (ETL, data lake, MDM) using Snowflake and Talend Cloud. Ensure data quality and governance, enable AI-readiness, pilot AI/ML proofs-of-concept, support reporting (Tableau/Power BI), and manage data operations and external vendors.

Design and build Snowflake transformation layer and migrate legacy SQL to versioned dbt models. Define conformed dimensions/facts using Kimball principles, set modeling and CI/CD standards, implement data quality/lineage, support BI migration and AI/ML readiness, and mentor future hires. Hands-on work in dbt, SQL, and Python with stakeholder collaboration and governance for financial and operational data.

Own and optimize data infrastructure and ETL/ELT pipelines using SSIS and Microsoft Fabric. Build and maintain SQL Server data warehouse staging, transformations, and lakehouse ingestion. Implement CI/CD, version control with GitHub, monitor pipeline health, troubleshoot data issues, document data flows, and translate stakeholder requirements into data engineering solutions that support BI reporting.