Top Data & Analytics Jobs

Lead and mentor a data engineering team to design, develop, test, and support cloud-based big data and streaming solutions. Build full-stack data pipelines using Java/Scala/Python, RDBMS/NoSQL, Redshift/Snowflake, and cloud platforms. Collaborate with product managers, perform unit tests and code reviews, and promote engineering best practices and tech community engagement.

Lead design and optimization of high-throughput data ingestion pipelines and Python-based microservices. Improve pipeline performance, reliability, and observability across distributed systems, define unified ingestion architectures, work with Snowflake for analytics, mentor engineers, and collaborate cross-functionally to ensure scalable, fault-tolerant data platforms.

The Senior Data Engineer will design and deploy AI solutions, build scalable data systems, and manage cloud databases while collaborating with teams to enhance decision-making processes.

As a Senior Lead Data Engineer, you will lead teams to design technical solutions, focusing on data integration and AI capabilities to support business needs, applying your expertise in cloud computing and big data technologies.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Design and operate metadata cataloging, lineage harvesting, governance workflows, and data quality for unstructured data. Configure and maintain Atlan/Anomalo/Unity Catalog, build automated checks and prototypes using Python/SQL/Databricks/Streamlit, collaborate with stakeholders, document standards, and drive governance tool adoption.

Scope, implement, validate, and monitor merchant data integrations to ensure accurate input to data science models. Maintain data configuration, create JSON/CSV templates, write Python/SQL/Spark/Databricks/R scripts for testing and transformation, upload historical data, triage alerts, and coordinate with internal and client teams to resolve data issues and improve pipelines.

Design, build, and maintain scalable batch and real-time data pipelines and data lake architecture. Improve observability and SLOs, optimize ETL/ELT, develop dbt workflows, support event-driven architectures, integrate financial APIs, and collaborate with analytics and ML teams to deliver reliable, model-ready data products.

Design, build, and maintain scalable data pipelines and cloud data infrastructure (warehouse, RBAC, validation). Collaborate with data scientists and engineers, orchestrate ML pipelines, ensure data quality/security, optimize cost and performance, lead incident response and mentor teammates.

The Senior Lead Data Engineer designs and develops technical solutions, leads a team of developers, and utilizes big data and cloud technologies to deliver robust solutions for Capital One.

Lead cross-functional Agile teams to design, build, test, and support cloud-based data solutions. Develop full-stack data engineering systems using Java/Scala/Python, RDBMS and NoSQL, Redshift/Snowflake, and distributed tools (Hadoop, Spark, Kafka). Mentor engineers, perform code reviews and unit testing, and collaborate with product managers to deliver scalable, performant data products.

As a Lead Data Engineer, you will collaborate in Agile teams to design and implement financial data solutions, utilizing extensive programming and cloud technologies.

As a Senior Lead Data Engineer, you will design, develop, and support data solutions, optimize systems, and collaborate within Agile teams to leverage technology for business transformations.

Lead a team of engineers to design and implement data solutions using cloud technologies, optimizing data pipelines and collaborating with Agile teams to deliver impactful data-driven products.

Lead Data Engineer role focused on building data solutions, collaborating across Agile teams, utilizing big data and cloud technologies, and mentoring team members.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Lead and mentor data engineering teams to design, build, test, and support cloud-based, full-stack data solutions. Work with big data, streaming, and data warehousing technologies (Redshift/Snowflake), collaborate with product managers, and ensure high-quality, performant code and systems.

Lead Data Engineer designs, develops, tests, and supports cloud-based data solutions. Collaborates with Agile teams, mentors engineers, implements big data and streaming systems, and optimizes code for performance to deliver data-driven products.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Design, build, and optimize cloud-native ETL/ELT pipelines using Azure Synapse, Databricks, and Snowflake. Develop healthcare data models (claims, EMR/EHR, HL7/FHIR), implement data governance and HIPAA controls, write optimized SQL and Python/Scala transformations, support analytics teams, monitor pipeline health, and document data lineage and architecture.

Develop, validate, and maintain Power BI reports and dashboards using DAX and Power Query. Extract, clean, and analyze data with SQL, Excel, Python and VBA to support supplier quality metrics (PPAP, CRV, Run at Rate, QPR). Gather requirements, enable drill-down reporting, schedule refreshes, deploy reports to production, and act as SME for supplier quality systems (SPPS, SQMS, SCMS). Hybrid role requiring in-office presence three days weekly.

As a Sr. Lead Data Engineer, you'll collaborate across teams to develop technical solutions, lead developers, and utilize big data technologies and cloud services to support data transformations.

Lead Data Engineer designs, develops, tests, implements, and supports cloud-based data solutions. Collaborates with agile teams, mentors engineers, builds streaming and batch pipelines using big-data and cloud technologies, and ensures performant, well-tested code.

Lead data and software QA for enterprise data platforms: define testing strategy, build automated test frameworks, validate ETL/ELT and data transformations, run data quality checks and reconciliations, test APIs and data products, analyze datasets with SQL to find defects, drive root-cause resolution, participate in Agile ceremonies, and mentor team members to improve quality practices.

Lead design, build, and optimization of large-scale real-time and batch data ingestion and transformation pipelines. Integrate enterprise systems (SFDC, NetSuite, Workday), ensure data quality, observability, and reliability, and enable analytics/AI workflows. Architect solutions using Airflow, dbt, Python, Kafka, Snowflake, apply DevOps/CI-CD practices, mentor global teams, manage multiple projects, and align data engineering initiatives with product and business stakeholders.