Top Data & Analytics Jobs

Designs and maintains automation for enterprise data governance, including APIs, ETL pipelines, metadata management, data profiling, validation, quality workflows, and data integrations. Develops reliable scheduled and event-driven solutions with monitoring, logging, retry logic, testing, CI/CD, and exception handling. Collaborates with stakeholders on governance roadmaps, tool requirements, and scalable data management improvements.

Build scalable data ingestion, transformation, and loading infrastructure using Spark, SQL, AWS, and Databricks. Develop entity resolution systems, CI/CD pipelines, anomaly detection, and high-quality data products. Design scalable processing and storage patterns, orchestration workflows, and data warehousing solutions while collaborating remotely and documenting technical designs.

Build and maintain dbt models and ETL/ELT pipelines on BigQuery and Daasity, connect marketing, inventory, and finance data, support KPI tracking and predictive models, deploy dashboards, and collaborate with AI and business teams to ensure clean warehouse outputs for analytics and automation.

Develops scalable data and analytics solutions using Microsoft Fabric, SQL, Python, and Power BI. Builds analytics-ready datasets, pipelines, semantic models, dashboards, and predictive or machine-learning models. Applies data governance, security, privacy, responsible AI, testing, documentation, and deployment practices. Partners with engineering, governance, institutional research, and business stakeholders to deliver trusted analytical products and actionable insights.

Designs, builds, and operates scalable cloud data platforms across Azure, Microsoft Fabric, and AWS. Develops Python ETL/ELT pipelines, containerized Azure Container Apps Jobs, data warehouses, dimensional models, and integrations between on-premises and cloud systems. Responsibilities include infrastructure as code, CI/CD, monitoring, security, cost optimization, documentation, stakeholder collaboration, and mentoring junior engineers.

Leads modernization of legacy data platforms and architects scalable batch and streaming pipelines on Azure and Databricks. Designs lakehouse and dimensional warehouse models, integrates REST APIs, implements data quality and observability frameworks, and establishes CI/CD and infrastructure as code. Uses OpenAI and Anthropic for AI-assisted engineering and validation. Mentors engineers, drives architecture decisions, and owns data reliability, governance, security, and performance.

Design, develop, migrate, deploy, and support scalable data pipelines, cloud data platforms, governance solutions, and Power BI reporting. Lead legacy-to-cloud migrations, build Airflow workflows and CI/CD pipelines, manage GitHub repositories, implement data quality and automated testing, and provide production support. Collaborate with finance, product, engineering, and business teams to deliver secure, reliable, and maintainable data solutions.

Leads the architecture, development, and delivery of enterprise data platforms using Snowflake, AWS, Python, Spark, Informatica, and modern ELT frameworks. Designs Data Vault and dimensional models, scalable ingestion pipelines, and cloud data architectures. Provides technical leadership, mentors engineers, manages delivery across initiatives, and ensures data quality, performance, governance, monitoring, and production reliability. Partners with business and technical stakeholders to translate requirements into robust data solutions.

Leads the design, development, and delivery of enterprise-scale data platforms, pipelines, and real-time, near-real-time, and batch integrations. Responsibilities include data transformation, quality controls, metadata, governance, testing, performance tuning, deployment, and compliance. Provides technical leadership, mentors data engineers, coordinates multiple projects, collaborates across teams, and develops solutions using Spark, Scala, Kafka, Hadoop, Starburst, Informatica, cloud technologies, and object storage.

Design, build, and operate high-throughput Go services and data pipelines powering a cybersecurity data lake. Own bronze, silver, and gold layers, including ingestion, enrichment, partitioning, compaction, schema evolution, and performance optimization. Translate threat intelligence workflows into reliable production systems, collaborate across teams, participate in on-call operations, and mentor engineers. The role requires distributed-systems expertise, experience with open table and columnar formats, object storage, ClickHouse, PostgreSQL, and large-scale datasets.

Leads end-to-end data engineering solutions, including scalable Snowflake and dbt pipelines, data modeling, SQL transformations, CDC, ELT, quality frameworks, monitoring, and production deployment. Designs engineering standards, troubleshoots complex data issues, improves performance and cost efficiency, collaborates with stakeholders, leads technical reviews, and mentors engineers.

Designs, builds, and evolves scalable data platform services, distributed systems, backend infrastructure, pipelines, and self-service tooling. Provides technical leadership for cross-functional initiatives, improves platform reliability and developer experience, and delivers reusable solutions supporting Unity’s advertising ecosystem and internal engineering teams.

Designs, develops, maintains, and optimizes complex data systems, SQL databases, ETL processes, data warehouses, reports, and .NET applications supporting pharmacy benefit management. Responsibilities include database performance tuning, troubleshooting pharmacy claims processes, developing scalable solutions, documenting technical designs, participating in Agile ceremonies, collaborating with stakeholders, mentoring teammates, and ensuring healthcare data compliance.

Lead the design, development, deployment, and operation of scalable Databricks batch and streaming data solutions. Build governed ingestion, transformation, quality, observability, and analytics pipelines using Python, PySpark, Spark, SQL, Delta Lake, and Unity Catalog. Establish engineering standards, optimize production workloads, implement infrastructure as code and CI/CD, support AI and machine learning data products, resolve technical issues, lead design reviews, and mentor engineers.

Build and operate production data and streaming systems across edge, hybrid, OT, IoT, and enterprise environments. Responsibilities include developing connectors, real-time event-driven pipelines, data contracts, schemas, validation, lineage, observability, telemetry, federated query patterns, and reliable integration platforms supporting analytics and AI. The role requires strong ownership, system design, documentation, incident readiness, and collaboration with engineering and customer-facing teams.

Designs and maintains data collection infrastructure, resilient pipelines, ETL/ELT processes, data warehouses, streaming architecture, BI reporting, and analytical systems. Troubleshoots technical defects, performs root-cause analysis, maintains specialized datasets, and owns end-to-end data collection, extraction, and cleansing. The role requires experience with AWS, Google Cloud, ETL tools, programming languages, NoSQL databases, and BI platforms.

Build and maintain batch data pipelines and ETL/ELT workflows using SQL, Python, APIs, databases, and files. Develop datasets, data models, quality checks, monitoring, documentation, tests, and deployment processes. Troubleshoot pipeline issues, support releases and backfills, modernize legacy processes toward secure cloud-based solutions, and provide business intelligence insights while collaborating with analysts, software engineers, and business partners.

Lead architecture, development, and optimization of a cloud-native data platform. Design high-volume batch and streaming pipelines, optimize data processing across AWS, Snowflake, Redshift, and Athena, and implement Iceberg-based data management. Establish security, privacy, observability, and DataOps practices while mentoring engineers, guiding technical direction, collaborating with data science teams, and delivering scalable petabyte-level data infrastructure.

Lead the architecture, development, and modernization of Prodege’s large-scale data platform. Build batch, ELT, streaming, and near-real-time pipelines supporting BI, experimentation, product analytics, machine learning, and AI applications. Establish data modeling, governance, observability, schema management, and data contract standards across Snowflake, dbt, Iceberg, Trino, and Kafka. Drive technical implementations, optimize performance and cost, mentor engineers, and partner with cross-functional teams to deliver reliable, scalable data systems.

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

Develops and implements data engineering and statistical analysis solutions for government customers. Responsibilities include automating data extraction and processing, building data models, analyzing large and diverse datasets, developing statistical methods, creating databases, reports, and visualizations, and communicating technical findings. The role requires independent problem-solving, data science and engineering experience, and support for intelligence-related data capabilities.

Build, optimize, and maintain scalable data pipelines and reusable datasets for business intelligence, marketing, and data science. Design a federated data validation framework, optimize queries, support data modeling and governance, and protect user privacy and security. The role uses Python, SQL, AWS, Airflow, Snowflake, PostgreSQL, and other modern data-stack technologies, with a hybrid schedule requiring three days weekly in the New York City office.

Develop and manage Workday PSA and Financials data solutions, including conversions, transformations, integrations, reporting, APIs, EIBs, and ETL processes. Create technical documentation, perform data analysis and loading, support migrations and M&A initiatives, and ensure security, compliance, access controls, and data quality. Partner with business analysts, functional leads, engineers, and security teams while managing multiple projects and serving as a Workday data subject matter expert.

Designs, develops, and operates secure, scalable data pipelines, platforms, integrations, and data models. Responsibilities include ETL/ELT development, data quality, observability, troubleshooting, architecture, cloud modernization, performance optimization, production support, and collaboration with technical and business stakeholders. The role also mentors engineers, establishes engineering standards, and supports long-term data strategy.

Provide senior-level data engineering for analytics: resolve complex technical issues, build decision-support reports and dashboards, lead projects, prepare and integrate data, coach staff, and ensure high-quality analytics to support academic, research, and healthcare goals.