Top Senior Data Engineer Jobs

Design, build, and maintain scalable data pipelines and integrations across AWS, Azure, and BigQuery. Normalize, aggregate, and ensure data quality for large web and transactional datasets, leveraging APIs, ETL/orchestration tools, and big-data technologies to support analytics and BI in an e-commerce environment.

Designs, builds, and supports scalable data platforms, ETL, APIs, and data solutions (on-prem and cloud). Collaborates with architecture, security, and LOBs; builds CI/CD pipelines; troubleshoots production incidents; mentors team members; and evaluates tools and technical strategies to enable analytics, data warehousing, streaming, and machine learning use cases.

Build and maintain scalable data pipelines for healthcare claims; implement de-identification and PII-reduction; ensure data quality and observability; integrate data stores and APIs; draft technical designs and documentation; gather product feedback and iterate quickly.

Lead performance investigations across the data product stack to diagnose and fix slow queries and UX issues. Design data architecture changes, build benchmarks, measure latency/throughput/cost (ClickHouse Cloud), prototype solutions, instrument monitoring, and partner with product and engineering to balance responsiveness, scalability, and cost.

Extract, transform, and load data from clients' legacy databases into CentralSquare systems. Write and tune SQL/ETL processes, present and validate converted data with clients, assist testing and go-live conversions, document processes, estimate project scope, and seek guidance from leadership when needed.

Design and build cloud-native, scalable data solutions for systematic trading. Partner with traders, quants, and architects to develop Snowflake-based ETL, data models, and pipelines; lead AI-driven capability adoption; ensure security and SDLC best practices.

Design, build, and support enterprise data platforms for analytics, reporting, and regulatory needs. Develop and optimize database objects and data models, automate data workflows with Python/Perl/Shell, provide Level 3 production support, improve data quality and performance, and leverage AI/LLM tools to enhance automation and analytics while embedding risk and control practices.

Design, build, and maintain scalable ETL/ELT data pipelines and cloud data platforms. Ensure data quality, governance, and availability for analytics and ML. Provide technical leadership, mentor engineers, and drive data platform modernization.

Serve as an experienced individual contributor and mentor on data engineering initiatives. Design and implement data architectures, ingestion pipelines, cloud databases, and visualizations. Lead smaller teams or projects, present technical designs to stakeholders, diagnose and resolve system issues, and help shape the program's data strategy and best practices.

The Staff Data Engineer will lead the architecture and scalability of H1's healthcare data processing systems, ensuring efficient entity resolution and big data operations. Responsibilities include design and optimization of distributed Spark pipelines, team leadership, and collaboration with AI/ML teams to enhance data processing capabilities.

Design, build, and productionalize batch and streaming data pipelines on GCP. Automate ETL/ingestion, curate data lake/warehouse assets, and develop reusable analytical data products. Collaborate with architects and stakeholders to translate business requirements into technical solutions and serve as a subject-matter expert in data engineering.

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.