Top Data & Analytics Jobs

As a Senior Lead Data Engineer at Capital One, you'll lead a development team, utilizing various big data and cloud technologies to deliver impactful solutions. Responsibilities include collaborating with Agile teams, mentoring, and ensuring optimal code performance.

As a Lead Data Engineer, you'll lead the design and implementation of scalable data platforms, build distributed streaming data pipelines, and collaborate on technical solutions using big data technologies and agile methodologies.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

The Data Governance Engineer advances data governance initiatives, focuses on metadata cataloging, lineage harvesting, and governance workflows, and collaborates on data quality and compliance.

Designs, builds, tests, deploys, and monitors scalable data solutions supporting AI agents, LLM workflows, and autonomous decision-making. Partners with engineering, AI/ML, product, and business teams on data architecture, semantic layers, knowledge graphs, governance, lineage, and data quality. Develops reliable pipelines and full-stack applications using cloud, streaming, backend, frontend, and database technologies. Leads technical solutioning while ensuring observability, auditability, performance, and scalable integration of structured and unstructured data.

Design, build, and operate end-to-end ELT data pipelines on Azure/Databricks. Implement DLT data quality checks, CDC, schema evolution, dimensional models for Power BI, monitoring, CI/CD, and operational runbooks while partnering with architects and BI stakeholders.

Summer internship focused on building scalable data architectures and pipelines, automating data processes, improving data quality frameworks, and supporting cloud infrastructure. Work includes AWS automation, data warehousing, workflow optimization, platform integration, and operationalizing tools to process structured and unstructured data while leveraging analytics, AI/ML, and standardized pipelines.

Designs and operates billion-scale distributed data platforms across streaming, near-real-time, micro-batch, and batch workloads. Owns advertising and marketing data domains, building reliable, scalable, tenant-aware pipelines with strong latency, quality, observability, privacy, and cost controls. Optimizes Spark, Kafka, BigQuery, Dataproc, storage, and orchestration systems. Leads architecture reviews, mentors engineers, improves incident response, and enables governed agentic workflows across global advertising products.

Own Serval’s complete data function as its first dedicated data hire. Build a unified warehouse, ETL/ELT pipelines, data models, quality and governance systems, documentation, and self-serve analytics. Partner with technical and business teams to define trusted metrics, consolidate product and SaaS data, and create AI-powered internal data tools. Select the data stack, establish engineering standards, and support scalable, cost-conscious infrastructure.

Builds and operates production data pipelines, source integrations, raw and conformed data layers, orchestration, monitoring, data quality tests, and advanced SQL transformations. Develops cloud warehouse models, supports curated and semantic layers, manages schema changes, documents lineage, reviews code, and collaborates with analysts, engineers, QA, and Snowflake administration. Requires at least three years of relevant experience and a bachelor’s degree or equivalent background.

Build and operate Lilly’s covered-entity pharmacy data platform on Databricks. Develop medallion architecture pipelines, data contracts, identity crosswalks, ingestion workflows, security controls, and governed data products handling PHI within a HIPAA trust boundary. Implement CI/CD, data quality, testing, monitoring, tokenization, and access controls while partnering with architects, analytics engineers, product owners, and policy engineers. Translate architectural designs into reliable, scalable production code and support governed AI and agent data consumption.

Designs, develops, and maintains data platforms, pipelines, integrations, models, and processing scripts. Identifies and resolves data quality issues, supports secure and reliable data environments, and monitors data lake performance and scalability. Builds ELT processes across repositories, APIs, on-premises systems, and cloud warehouses while collaborating with cross-functional teams to deliver analytical and operational solutions.

Develop and maintain data engineering tools, services, ETL pipelines, ingestion processes, data transformations, extraction tools, and enterprise data models. Analyze structured and unstructured source data, optimize query performance, process large data volumes, collaborate with data scientists, and provide project documentation.

Owns clinical data operations by building and maintaining ELT pipelines, warehouse models, data contracts, quality controls, reconciliations, dashboards, and monitoring. Integrates clinical, laboratory, sequencing, and corporate data into validated, audit-ready systems. Supports production reliability, partner data transfers, regulatory compliance, root-cause analysis, documentation, stakeholder collaboration, and technical guidance for research and development teams.

Build and maintain scalable batch and real-time data pipelines and services for finance systems. Develop ETL workflows, data models, APIs, and distributed processing solutions using Spark, cloud platforms, and SQL. Ensure data governance, security, and quality while collaborating with engineers, product teams, and business stakeholders to troubleshoot, optimize, and improve system reliability.

Develop data solutions by translating technical specifications into code, querying and processing large datasets, integrating data from multiple sources, and testing solution quality. Collaborate with business and technical teams to ensure implementations meet business objectives. The role requires SQL, Python, Spark, data analysis, coding, quality testing, Agile practices, and regular on-site work near Costa Mesa, California.

Build and maintain production data pipelines across ingestion, orchestration, transformation, and modeling using Python, Airflow, dbt, Snowflake, SQL, Terraform, and AWS. Responsibilities include debugging production data issues, improving reliability, participating in code reviews and architecture discussions, and learning secure, compliant practices for sensitive healthcare data.

Designs, develops, and optimizes enterprise Snowflake data platforms, pipelines, data models, and ELT/ETL workflows. Builds reliable data lake and warehouse solutions, manages Snowflake performance and costs, implements data quality and governance practices, and supports cloud data environments on Azure or AWS. Partners with analysts, scientists, engineers, and business stakeholders to deliver scalable solutions, troubleshoot platform issues, influence architecture, and mentor junior engineers.

Build and operationalize scalable cloud data pipelines and infrastructure, primarily on GCP. Responsibilities include ETL/ELT development, complex data integration, data architecture, process automation, analytics tooling, performance optimization, CI/CD support, and collaboration with engineers, data scientists, product managers, and business stakeholders. The role focuses on healthcare and clinical data, including data platforms, operational systems, and infrastructure supporting business insights.

Designs, builds, and scales cloud-based batch and streaming data platforms and pipelines. Owns data workflow reliability, performance, testing, monitoring, and quality. Implements modern data stack solutions using dbt, Databricks, AWS, Snowflake, Airflow, Fivetran, Kafka, and Kinesis. Collaborates with cross-functional teams to translate business needs into scalable data solutions, while leading projects, influencing stakeholders, mentoring peers, and promoting engineering best practices.

Build and maintain reliable data ingestion, transformation, and modeling pipelines using Python, Dagster or Airflow, dbt, Snowflake, Airbyte, and AWS. Responsibilities include API and PostgreSQL extracts, Parquet and Snowflake loads, production monitoring, troubleshooting, backfills, incremental modeling, code reviews, Terraform updates, documentation, and runbooks. Partner with analytics and product teams on dimensional modeling, data quality, and pipeline reliability.

Develop and maintain ETL/ELT data pipelines using Databricks, PySpark, SQL, dbt, and Snowflake. Apply Snowflake performance tuning, Unity Catalog, data quality frameworks, and enterprise SaaS integrations including Salesforce, D365, Coupa, Workday, and Concur. Candidates should understand LLMs, RAGs, and AI agentic tools, with strong Python and SQL skills. The position requires 12 or more years of overall experience, U.S. citizenship, and local availability.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

Lead design and deliver enterprise data governance: implement data models, metadata standards, data cataloging/lineage, governed ETL/ELT pipelines, data quality controls, and coach teams on AI-assisted engineering and secure practices.