Top Data & Analytics Jobs

Build and operate high-volume Python data ingestion pipelines orchestrated with Apache Airflow. Model and serve large datasets in ClickHouse and Snowflake, migrate pipelines to S3 and ClickHouse, maintain enterprise data deliveries, and implement monitoring and data quality checks. Collaborate with engineering, product, and data science teams to develop data products. The role is hybrid, requiring three to four days weekly in San Mateo or New York.

Designs, builds, and operates scalable cloud data platforms, pipelines, and applications. The role uses Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases to deliver resilient data solutions. Responsibilities include data modeling, distributed processing, security controls, real-time pipelines, architecture standards, stakeholder communication, mentoring, and collaboration with product and engineering teams.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Collaborate with Agile teams, product managers, software engineers, analysts, and data scientists to deliver secure, resilient data solutions. Apply Python, Spark, SQL, relational and NoSQL databases, Databricks, Snowflake, and cloud technologies. Establish engineering patterns, data security controls, observability, and operational best practices while mentoring team members and communicating technical outcomes to stakeholders.

Designs, builds, and operates scalable cloud data platforms, pipelines, and applications. Leads architectural decisions, evaluates technologies such as Snowflake and Databricks, enforces reusable data engineering patterns, and delivers resilient solutions. Collaborates with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical concepts to stakeholders. Drives large-scale data initiatives independently across distributed, relational, non-relational, real-time, and warehousing environments.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Lead architectural decisions, establish reusable engineering patterns, evaluate technologies such as Snowflake and Databricks, and deliver distributed, real-time, and warehouse solutions. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical outcomes to stakeholders.

Build and deploy scalable data engineering solutions supporting AI and machine learning applications for defense and intelligence customers. Responsibilities include data movement, transformation, storage, ETL pipeline development, database integration, data availability, deployment scalability, and collaboration with software engineers and data scientists. The role uses Databricks and AWS in Agile environments and requires onsite work, active Secret clearance, U.S. citizenship, and willingness to obtain TS/SCI.

Migrates customer data from legacy databases, reports, and file structures into an ERP system. Develops Java- and Python-based conversion programs and SQL tools, maps legacy data, validates conversion accuracy, supports multiple project teams, manages competing priorities, and provides customer service during implementations.

Build and maintain scalable data platforms, pipelines, and analytics applications for mission-driven clients. Develop production-quality back-end and front-end solutions using data engineering, software development, visualization, and cloud technologies. Collaborate with multidisciplinary teams and customers, translate requirements into technical solutions, analyze disparate data, manage development risks, and deliver insights in agile and classified environments.

Designs and maintains scalable SQL and NoSQL database systems and cloud-native data platforms. Builds real-time and batch processing pipelines, data lakes, data meshes, dashboards, migration tools, and streaming architectures. Optimizes partitioning, sharding, replication, queries, indexes, capacity, security, and data models for high-volume workloads. Collaborates with product and engineering teams, supports data governance and quality, troubleshoots distributed systems, and implements CI/CD for database and pipeline deployments.

Designs, develops, and deploys scalable data pipelines, preprocessing workflows, feature engineering processes, machine learning services, and decision-support tools in secure environments. Collaborates with product managers, developers, platform engineers, and mission stakeholders. Applies data validation, model testing, visualization, storytelling, agile development, reproducibility, and continuous delivery practices within mission-critical Department of Defense environments.

Build and maintain scalable Snowflake data pipelines and dbt-based ELT workflows using SQL and Python. Integrate multiple source systems, implement Medallion Architecture patterns, monitor pipeline reliability, resolve data quality issues, and support performance and cost optimization. Collaborate with analytics, product, and business teams to deliver trusted datasets while contributing to data modeling, governance, testing, documentation, lineage, code reviews, and deployment practices.

Lead the design and evolution of BJ’s Databricks Lakehouse, building batch and real-time pipelines, integrating digital and operational data, and establishing data quality, governance, CI/CD, and cost optimization. Develop AI-powered analytics and Databricks agents, own the digital data roadmap, and mentor a distributed engineering team while partnering with product and business leaders.

Develop and maintain data warehouses, ETL solutions, and data acquisition jobs using Oracle and Microsoft SQL Server technologies. Collaborate with institutional research, data governance, data science, and IT teams to support reporting and university initiatives. Improve software development, testing, source control, and release processes; monitor data workflows; design project solutions; communicate progress; and stay current with data engineering, business intelligence, and cloud technologies.

Develop and maintain scalable data pipelines, integrations, data lake structures, and analytical solutions for operational risk intelligence. Analyze incidents and risk events, identify trends and control gaps, and produce dashboards, reporting, and executive-ready insights. Troubleshoot data and workflow issues, improve automation and data quality, support governance and security standards, and collaborate with engineering teams and stakeholders on operational intelligence improvements.

Design, build, and maintain the company’s modern data warehouse and scalable ETL/ELT pipelines. The role focuses on warehouse architecture, data modeling, schema evolution, infrastructure selection, performance optimization, pipeline monitoring, data integrity, API integrations, and support for business intelligence analytics. The position is fully onsite in West Los Angeles and requires U.S. Person status.

Designs and maintains scalable data pipelines and infrastructure, establishes data governance and quality standards, and drives technical decisions across Headway’s data platform. Partners with Product, Engineering, Analytics, Data Science, Infrastructure, Finance, and Insurance Systems to improve architecture, performance, reliability, security, and AI-readiness. Ensures data is complete, traceable, trustworthy, and suitable for powering internal tools and AI-driven insights.

Build and maintain ETL/ELT pipelines that ingest, transform, secure, and publish structured and unstructured data. Design data models, troubleshoot pipeline issues, support data quality, and deliver analytical reports and dashboards using Cognos and Tableau. Collaborate cross-functionally in an agile environment, manage report and pipeline change requests, and support data discovery across cloud environments and third-party applications.

Build and architect scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, Databricks, Snowflake, and relational or NoSQL databases. Design secure, resilient, and reusable data engineering solutions; support distributed and streaming workloads; enforce engineering standards; collaborate with product, software, analytics, and data science teams; communicate technical concepts; and mentor other engineers.

Build and architect scalable cloud data solutions, pipelines, platforms, and data products. Lead large-scale data initiatives, make architecture and platform decisions, ensure performance and operational efficiency, and apply modern technologies such as Python, Spark, Databricks, and Snowflake. Collaborate with Agile teams, product managers, software engineers, analysts, and data scientists while mentoring engineers and communicating technical concepts to stakeholders.

Design, build, and support scalable cloud data platforms, pipelines, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, data security, observability, orchestration, and real-time pipelines. It also requires influencing cross-functional teams, mentoring data professionals, establishing engineering standards, and communicating technical outcomes to stakeholders.

Designs, builds, and maintains reliable data pipelines and platforms in AWS environments. Develops ETL/ELT workflows, data models, integrations, quality checks, monitoring, and infrastructure for analytics, applications, and AI initiatives. Collaborates with data scientists, analysts, and software engineers while documenting solutions and troubleshooting production systems. The role is remote within the continental United States and emphasizes Python, SQL, orchestration, relational databases, and cloud data infrastructure.

Leads the design, development, deployment, and operations of enterprise data platforms and scalable batch, streaming, and real-time pipelines on Google Cloud. Mentors data engineers, establishes engineering standards, and ensures data solutions are secure, governed, reliable, observable, scalable, and cost-efficient. Responsibilities include orchestration, CI/CD, infrastructure as code, production support, data quality, metadata management, stakeholder collaboration, and evaluation of AI and generative AI capabilities for engineering automation and platform improvement.

Build the company’s foundational data platform, including ingestion pipelines, record matching, reconciliation tools, and a flexible data warehouse. Partner with the CTO to establish the core system of record and automate customer onboarding. Translate complex data concepts for nontechnical stakeholders while shaping product direction in an early-stage environment. Bonus responsibilities include integrating AI model services and supporting business intelligence dashboards and visual reporting.

Build and scale programmable data infrastructure supporting Vesalius Therapeutics’ human cellular biology platform. The role contributes to large-scale genetics, genomics, stem cell, cellular phenotyping, and artificial intelligence initiatives designed to uncover causal biology and identify therapeutic intervention points.

Designs, builds, and scales data pipelines across raw, staging, curated, and serving layers. Develops Azure Data Factory ingestion and transformation workflows for APIs, files, and semi-structured data in Snowflake. Implements resilient extraction, validation, testing, schema enforcement, and backfill processes. Supports CI/CD deployments, establishes data engineering standards, provides technical direction, and mentors engineers while collaborating with analytics, product, and engineering teams.