Top Data & Analytics Jobs

The Data Engineer will design, build, and maintain data pipelines and cloud solutions, ensuring data quality and supporting analytics. Key tasks involve developing ETL processes, managing CI/CD, and collaborating with teams for data integration.

As a Data Engineer, you will build and maintain data ingestion pipelines, develop data transformations, and migrate SQL Server processes to Azure-native solutions, supporting marketing campaigns and analytics teams.

Design, build, and maintain scalable data pipelines and platforms for banking data. Collaborate with teams to ensure secure, reliable data products.

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 operate scalable data governance solutions (metadata, lineage, quality) using Databricks, Snowflake, and Collibra. Develop and optimize data pipelines, manage cloud-native deployments (Kubernetes/Docker), provide on-call support, diagnose performance and job failures, ensure PCI/PII controls, and collaborate with cross-functional teams to support governance across cloud environments.

Lead design and implementation of scalable data pipelines and warehouses, establish data quality frameworks, mentor engineers, drive technical architecture and tooling decisions to support insurance analytics and operations.

Design, build, and optimize data products for credit risk and fraud management. Collaborate with data scientists and stakeholders to implement scalable data solutions.

This role involves leading data infrastructure modernization efforts, architecture reviews, and migration planning while providing technical mentorship and establishing operational standards.

The Senior Data Engineer will maintain and improve data infrastructure, design ETL processes, and create high-performance pipelines for analytics and AI initiatives using SQL and Python in cloud environments.

Design, build, and operate end-to-end ELT data pipelines on Azure/Databricks using PySpark, Delta Lake and DLT; implement data quality, CDC, schema management, orchestration, monitoring, and publish analytics-ready datasets for Power BI while supporting governance, CI/CD, and operational runbooks.

Senior Data Engineer responsible for designing, implementing, and operating high‑performance relational data solutions for the MIR Data Platform. Duties include schema and metadata design, event‑driven processing with SQL Service Broker, performance tuning, supporting reporting/audit use cases, cloud migration readiness (AWS/Azure), and production incident resolution.

Design, build, and maintain Snowflake-based data warehouse and lakehouse pipelines using dbt and SQL. Optimize performance, ensure data quality, support CI/CD and orchestration, collaborate on cloud infrastructure, document models and runbooks, and troubleshoot pipeline issues.

Design, build, and maintain San Francisco's central data infrastructure and pipelines. Administer Snowflake, implement IaC with Terraform, build streaming and batch ETL/ELT, optimize SQL and warehouse performance, monitor platform health, and collaborate with analysts, data scientists, and city stakeholders to ensure secure, reliable data access.

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.

Support government customer by extracting, cleaning, modeling, and analyzing large datasets; automate ETL and statistical workflows; develop/validate data models and novel statistical approaches; produce reports, visualizations, and data packages; operate independently on data science/engineering tasks and support capability acquisition.

Design, build, and maintain Databricks-based data pipelines and ETL/ELT processes using Python and SQL. Optimize workflows for performance and cost, support data modeling and cloud integrations (Azure/AWS/GCP), monitor and troubleshoot pipelines, document best practices, and collaborate with cross-functional teams.

Senior Data Modeler responsible for designing logical and physical data models, implementing database designs, planning data collection/cleansing, coordinating documentation, and partnering with data transformation teams to enable ETL/ELT pipelines, hybrid on-prem/cloud data movement, and reporting solutions.

Design, build, and maintain scalable data pipelines and models; collaborate with analysts and data scientists to provision prepared datasets; implement data quality, governance and monitoring; recommend technologies; lead root-cause analysis, code reviews and testing; mentor teammates and support production data environments.

Lead design and implementation of scalable, cost-optimized data pipelines and real-time streaming solutions. Drive data architecture, governance, security (row/column-level), ETL orchestration, and enterprise visualization. Mentor junior engineers, engage vendors, optimize cloud costs, and collaborate with stakeholders to deliver strategic, data-driven insights for logistics operations.

Lead design and delivery of Microsoft Fabric and Azure-based data lakehouse and warehouse solutions. Own data ingestion, ETL, modeling, governance, security, CI/CD, and Power BI integration. Architect GenAI/RAG solutions, mentor a small engineering team, define standards and playbooks, and partner with business and tech stakeholders to execute multiple concurrent implementations.

Lead ownership of data sourcing, modelling, and distribution for compliance and reporting. Define and implement a central data foundation, set standards, ensure data quality, guide teams, and document end-to-end data flows to enable consistent, reusable reporting solutions.

Design, build, and maintain end-to-end ETL pipelines and Control-M workflows for pension administration modernization. Perform source-to-target mappings, VSAM-to-SQL conversions, COBOL field extraction, advanced SQL development, data profiling, metadata management, and data quality rule implementation. Support data warehouse, reporting, and defect resolution while collaborating with SMEs and technical teams to remediate data issues and document lineage and dictionaries.

Design, build, and maintain secure, source-controlled ELT/ETL pipelines and data architecture in Azure (Synapse, Azure ML). Optimize ingestion/storage (Parquet), add monitoring/error handling, enable self-service for analysts, collaborate with data scientists, document assets, and evaluate AI/LLM-assisted automation.

Design, build, and operate data pipelines for structured and unstructured sources, performing ingestion, cleaning, transformation, quality checks, lineage capture, and monitoring. Ensure compliance with DoD governance and security, troubleshoot pipeline failures, maintain runbooks, and collaborate with stakeholders to deliver scalable, governed data products on Army enterprise platforms using Vantage/Foundry tools.

Design, build, and maintain scalable data pipelines and platforms; assess and clean data; create interactive dashboards and visualizations; mentor multidisciplinary teams; collaborate with stakeholders to clarify reporting requirements; deploy data solutions to support mission-driven projects. Requires Secret clearance and a bachelor’s degree.