Top Senior Data Engineer Jobs

Designs and implements big-data data management solutions (Cloudera/Hortonworks/Databricks). Builds and automates reporting and analytics using SQL and Databricks, optimizes queries, manages ETL, integrates multiple data sources, enforces data governance, and supports payment integrity reporting requirements.

The Software Engineer will build and operate the cloud infrastructure for data ingestion, collaborating with scientists to improve dataset quality, scale, and cost efficiency for AI model training.

Lead Data Engineer to design and build scalable ETL pipelines and data models using Azure cloud (Databricks, Synapse, Data Factory) and big data technologies (Spark, Delta/Parquet). Support real-time and batch lakehouse/warehouse analytics, data quality, and enable datasets for AI/ML and business decision-making.

Design, build, and migrate large-scale batch data pipelines into Databricks. Develop and optimize production-grade Python/Spark/SQL solutions, support parallel legacy and cloud systems during migration, lead technical design discussions, use AI tools to improve workflows, ensure pipeline reliability through testing/monitoring/tuning, and participate in a rotating on-call schedule.

Design and build scalable data lakes, warehouses, and lakehouses; implement Python ETL/ELT pipelines; orchestrate workflows with Airflow; ingest data from 3rd-party APIs; optimize columnar file formats and SQL performance; support ML initiatives; consult with stakeholders and build AWS/on-prem infrastructure using Terraform.

Lead the design, build, and maintenance of scalable data pipelines, architectures, and data products on Databricks and AWS. Partner with cross-functional teams, mentor engineers, implement CI/CD, optimize performance, and enforce data governance to support analytics and AI initiatives.

Design, build, and operate scalable batch and streaming data pipelines using Apache Spark and cloud lakehouse platforms. Develop ETL/ELT workflows, data models, CI/CD for data workloads, ensure data quality and performance, monitor and troubleshoot jobs, and collaborate with analytics, platform, security, and product teams to deliver analytics-ready datasets.

Own design and implementation of a greenfield analytical data platform: ingesting PostgreSQL and Kafka streams, establishing governance, data lineage, quality, monitoring, and compliance-ready pipelines. Evaluate AWS-native tooling, Snowflake, and Databricks; produce RFCs and documentation; mentor junior engineers and ensure analytical systems remain downstream of transactional sources.

Design and build scalable data lakes, warehouses, and lakehouse architectures. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest from third-party APIs, optimize columnar storage formats, and support ML initiatives using Spark, Snowflake, and cloud platforms. Serve as a technical consultant translating business needs into data architecture roadmaps.

Lead design and implementation of a scalable enterprise data warehouse: dimensional modeling, ETL/ELT pipelines, cloud data platforms, data governance, performance tuning, monitoring, and mentoring. Collaborate with stakeholders and engineering teams to enable analytics, ensure data quality and security, and support data science/ML workloads including feature engineering and NLP components.

Design, build, and maintain scalable ETL/ELT, batch and streaming data pipelines and platforms. Create reusable data models (dbt/Spark), ensure data quality and observability, collaborate with data scientists, analysts, and product teams, and mentor junior engineers while advancing data platform architecture and tooling.

Design and build scalable data lakes, warehouses, and lakehouse architectures. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest third-party APIs, optimize columnar formats and query performance, and consult with stakeholders to translate business goals into data architecture roadmaps using Spark, Snowflake, and cloud infrastructure.

Design, develop, and implement Microsoft Azure Databricks data platforms and pipelines to ingest, cleanse, transform, and publish data. Assemble large datasets, collaborate with architects and leads to ensure fit-for-use data, apply coding standards, and drive process improvements.

Design, build, and own large-scale ETL pipelines to produce production-grade analytics datasets. Translate requirements into specs, implement and validate data products, ensure data quality, debug distributed systems, and collaborate cross-functionally with data science, backend, frontend, and product teams for production rollouts.

Design, build, and operate large-scale ETL pipelines to turn high-volume raw data into production-grade datasets for reporting and measurement. Own data products end-to-end from requirements through implementation, validation, and production rollout, collaborating with data science, backend/frontend engineers, and product. Ensure data quality, performance tuning, and operational reliability.

Design, build, and operate large-scale ETL pipelines on Databricks/Delta Lake to produce curated analytics datasets. Collaborate with data science, backend/frontend engineers, and product from requirements through production, ensuring data quality, validation, performance tuning, and incident resolution.

Lead data engineering work across migrations, pipelines, integrations, and APIs. Design, build, deploy, test, monitor, and support production data solutions while ensuring data quality, validation, and adherence to architectural standards in an agile continuous-delivery environment.

Lead design and scaling of backend data infrastructure for Beacon's Datastore: build event-driven pipelines, data models, and APIs; draft RFCs; integrate services; improve operational robustness, documentation, and tooling to support clinical and scientific workflows.

Design, build, and maintain scalable data pipelines and ETL for real-time marketing applications. Collaborate with product and analytics teams, ensure data quality, reliability, and performance, troubleshoot issues, and contribute to design and code reviews and documentation.

The Senior Data Engineer will design and implement data processing pipelines using Java and Spark, optimize performance, and mentor junior engineers while ensuring compliance with data governance standards.

As a Senior Data Engineer, you'll design and implement ETL/ELT pipelines, write Python code for data processing, and optimize SQL models. You'll collaborate with teams and improve data architectures to support AI and marketplace strategies.

The Senior Data Engineer at BeyondTrust designs and manages data infrastructure, creates ETL pipelines, and optimizes database performance while ensuring data governance.

Founding data engineer to design and build Payabli's data platform: architect lakehouse/warehouse, build batch and streaming pipelines, model canonical datasets, ensure data quality/observability, enforce access/masking/lineage for regulated financial data, and enable analytics/ML feature pipelines while establishing team standards and CI/CD.

The Senior Data Developer Tech Lead will design and manage scalable data solutions within Hadoop, lead streaming data architecture, optimize performance, and integrate AI/ML capabilities, while coaching team members and ensuring best practices in the banking domain.

Lead the design and implementation of Hadoop architecture and real-time data solutions. Coach team members, optimize performance, and ensure data governance within a small agile squad, focusing on AI/ML capabilities.