Top Data & Analytics Jobs

Designs, builds, and operates reliable Azure data pipelines using Databricks, Azure Data Factory, PySpark, Spark SQL, and Delta Lake. Responsibilities include batch and streaming ingestion, ELT transformation, dimensional modeling, data quality enforcement, CDC, schema management, monitoring, alerting, governance, CI/CD, documentation, and operational support. Partners with architects, BI developers, and stakeholders to deliver analytics-ready datasets and improve pipeline performance, reliability, and cost efficiency.

Designs, develops, and analyzes data management systems for a U.S. government customer. Builds batch and streaming data pipelines, performs ETL on large unstructured datasets, and maintains CI/CD pipelines for data and platform services. Collaborates with developers, data scientists, subject matter experts, and cyber analysts. Uses Kafka, Spark, Prefect, Docker, Kubernetes, Linux, and DevOps practices.

Designs and maintains enterprise ETL and data integration pipelines, develops scalable data warehouse solutions, optimizes SQL and data workflows, and supports migration of legacy platforms to Google Cloud. The role focuses on data quality, performance, reliability, troubleshooting, and collaboration with technical and business stakeholders.

Analyze product, engineering, and machine data; build Python and SQL analyses; manage ETL pipelines across Amazon S3, Google Cloud Storage, and BigQuery; and develop Tableau or Streamlit dashboards. Translate stakeholder needs into KPIs, metrics, reliable datasets, and actionable insights while monitoring data quality, troubleshooting pipeline issues, and enabling self-service analytics.

Own and scale the company’s analytics infrastructure, including BigQuery data warehouses, dbt data marts, external data exchange, DataOps, data quality, governance, security, and cloud cost optimization. Partner with application database teams, analytics engineers, and data scientists to enable reliable metrics and scalable client data delivery.

Designs, builds, and operates scalable enterprise data platforms supporting ingestion, processing, storage, governance, analytics, and AI/ML. Responsibilities include developing batch and real-time pipelines, lakehouse and warehouse architectures, orchestration, infrastructure automation, CI/CD, monitoring, security, cost optimization, and observability. The role partners with data, ML, DevOps, security, and application teams, troubleshoots complex platform issues, establishes reusable engineering standards, contributes to architecture strategy, and mentors engineers.

The Data Engineer II designs, develops, and maintains data integration and analytics solutions, ensuring data quality and accessibility for business stakeholders, while collaborating with teams to meet business needs.

The Data Engineer will design, develop, and implement data solutions, ensuring data integrity and availability, collaborating with stakeholders to meet business analytics needs.

Build and maintain scalable web crawlers to extract, validate, and structure product data from eCommerce sites. Use AI-driven techniques, browser automation, proxies, and APIs to handle dynamic content and anti-bot measures. Monitor performance, clean large datasets, collaborate with Product/Engineering/Data teams, and continuously improve crawling frameworks and automation.

Design, build, test, and maintain data architectures, database systems, and pipelines using GCP and BigQuery. Optimize data retrieval, automate processes, redesign infrastructure for scalability, collaborate with stakeholders, and deliver analytics tools that provide actionable business insights.

Designs and delivers scalable, low-latency streaming data pipelines using Apache Flink, Confluent Kafka, and Apache Iceberg. Modernizes batch and RDBMS workflows into event-driven architectures, builds lakehouse data structures, ensures data quality, optimizes streaming performance, and integrates ITSM and enterprise systems. The role also leads testing, documentation, delivery lifecycle activities, stakeholder collaboration, CI/CD, operational monitoring, and mentoring of data engineers.

The Data Engineer is responsible for building and maintaining data pipelines, ensuring data quality and reliability, and collaborating with various teams on data needs. This involves working with AWS tools and programming in Python and SQL to support analytics and infrastructure.

Design, build, and optimize end-to-end data pipelines and dataflows, integrate with AWS, maintain schemas/data dictionaries, provide Tier 3 support, collaborate with stakeholders and product teams, document architectures, and support off-hours deployments. Ensure data governance, scalability, and production readiness while working primarily on-site in a cleared defense environment.

Design and build scalable cloud-native data pipelines and models to produce trusted, analysis-ready data. Ensure data quality, governance, and privacy controls, apply software engineering best practices, enable BI and AI/ML consumption, and collaborate with clients and cross-functional teams.

Design, develop, and manage scalable database systems, data pipelines, structures, and load routines primarily in AWS RDS for PostgreSQL and SQL environments. Support application teams through APIs, database optimization, data processing, and reporting tools such as Power BI. Collaborate with technical and non-technical stakeholders in an Agile setting. The role requires U.S. citizenship and an active TS/SCI with Polygraph clearance.

Design, develop, and optimize scalable batch and real-time data pipelines on GCP. Implement Medallion Architecture, build Python and PySpark transformations, optimize BigQuery and SQL workloads, deploy Dataflow pipelines, and orchestrate workflows with Cloud Composer and Airflow. Manage GCS storage, CI/CD, version control, IAM, data governance, security, reliability, scalability, and cost efficiency across enterprise data platforms.

Provides technical leadership for enterprise data platform engineering. Designs and implements ETL, ELT, streaming ingestion, data models, cloud data lakes and warehouses, monitoring, data quality, governance, testing, version control, and CI/CD practices. Leads AWS, Snowflake, and dbt implementations while integrating disparate data sources for analysts and data scientists. Partners with business stakeholders to align data products with organizational strategy and enables self-service analytics and automation.

The Senior Research Engineer will curate large datasets, automate data pipelines, optimize queries, and support data-driven research impacting business outcomes.

Build and operate Numeric’s financial data platform, including scalable ingestion pipelines, typed data contracts, lakehouse schema management, data security, event-driven processing, observability, and reporting infrastructure. Work across TypeScript services, SQL, ClickHouse, Postgres, and transformation tools while partnering closely with accounting, reporting, and AI teams.

Build and operate Numeric’s financial data infrastructure, including ingestion pipelines, typed data contracts, lakehouse schemas, batch and event-driven processing, security controls, observability, and scalable reporting. Work across TypeScript, SQL, and Python while collaborating with accounting, reporting, and AI teams to deliver reliable, auditable, multi-tenant financial data systems.

Build and extend automated enterprise data governance and quality frameworks. Define measurable trust rules, data contracts, validation and certification workflows, metadata, lineage, tagging, and trust scoring across a cloud data platform. Integrate governance checks into developer workflows and Collibra, coach engineering and product teams, and establish organization-wide data trust standards. The role requires strong SQL, BigQuery or comparable warehouse experience, and expertise with data quality or observability tools.

Lead the design, development, scaling, and operation of enterprise data platforms, services, and pipelines. Own mission-critical systems, deliver complex multi-team projects, build analytics solutions, and improve data delivery processes. Partner with architects, engineers, analysts, data scientists, and business stakeholders to create reliable, observable, secure, and governed platforms. Provide technical leadership, evaluate technologies, establish engineering best practices, mentor engineers, and guide technical decisions across teams.

Design, develop, and optimize cloud-based ETL/ELT pipelines and data architectures using Informatica and Databricks in AWS GovCloud. Author and optimize SQL and Python scripts, build secure RESTful APIs, perform data validation, migrations from on-premises to cloud, and troubleshoot performance issues across large datasets. Support DON Jupiter enterprise environment and related ETL frameworks.

Build, deploy, and maintain secure, scalable data pipelines and analytics on AWS/Databricks. Implement CI/CD, governance (Unity Catalog, lineage), platform observability, and operational support for enterprise datasets and Databricks SQL reporting.

Lead design and delivery of scalable data pipelines and APIs for Digital Risk Protection. Mentor engineers, align and ingest multiple data streams (including graph data), productize analytics and LLM outputs, and drive DRP technical projects with stakeholders to enable data-driven intelligence.