Top Data & Analytics Jobs

Design, architect, and deliver scalable data pipelines and storage solutions using big data and cloud technologies. Lead technical strategy, mentor engineers, ensure data governance, security, and performance, and collaborate with Data Science and Product teams to support analytics and ML initiatives.

Lead end-to-end data engineering on GCP: design scalable data platforms and architectures (lake/lakehouse/warehouse), build and optimize BigQuery/Spark pipelines, ensure data quality/security, enable ML workflows with Data Science, mentor engineers, and own delivery and performance.

Design, build, and optimize data pipelines and storage solutions. Ensure data accuracy and performance, provide technical support, and collaborate with data scientists and stakeholders to resolve data issues and meet requirements.

Design, build, and optimize scalable AWS-based data pipelines, data lakes, and warehouses. Migrate legacy systems, enforce data quality/security/governance, implement ETL/ELT and big-data solutions, adopt CI/CD and IaC, collaborate with stakeholders, and provide technical leadership and troubleshooting.

Design, build, and maintain scalable ETL/ELT data pipelines and BigQuery data models. Implement Airflow workflows, CI/CD for deployments, ensure data quality and performance, and collaborate with data scientists, analysts, and business teams to deliver reliable analytics-ready data.

Lead architecture and delivery of a cloud-native, petabyte-scale data platform: define technical roadmap, mentor engineers, build ETL/ELT pipelines, enable real-time streaming, implement Iceberg table format, optimize Snowflake/Redshift/Athena compute, and integrate observability with Splunk.

Build and maintain scalable data pipelines and enterprise DataMarts, apply advanced SQL to transform disparate sources into well-modeled, high-quality datasets, ensure data governance and integrity, optimize performance, and partner with analysts and stakeholders to enable reliable reporting and analytics.

Design, build, and maintain scalable data pipelines on AWS using EMR, Spark, and PySpark. Optimize EMR clusters and Spark jobs, implement ETL/ELT processes, ensure data quality, automate ingestion, and collaborate with data scientists and analysts. Document infrastructure, participate in code reviews, and monitor performance and costs.

Design, build, and maintain ELT pipelines, data storage, quality, lineage, and observability for enterprise systems. Support AI/ML feature pipelines and governed knowledge layers, collaborate with stakeholders, and ensure compliance with FDA GMP, ITAR, and DCAA. Implement CI/CD, source control, and modern development practices to improve operational efficiency and data-driven decision making.

Design, build, and maintain operational and analytical data platform capabilities using Java Spring Batch, Python, AWS, Oracle, and Snowflake. Deliver end-to-end development, production rollout, and support; improve financial product data quality, modeling, and SQL performance; collaborate with business partners and squads.

Design, build, and maintain ETL/ELT data pipelines and orchestration using Airflow, Python, and PySpark. Develop custom connectors, implement DataOps and CI/CD for data workflows, monitor and troubleshoot pipelines, validate data quality, and document processes while collaborating with cross-functional teams and supporting large-scale distributed data systems.

Design, build, modernize, and maintain operational and analytical data capabilities for Wealthscape Reporting, Analytics and Insights. Perform solution design, data analysis, ETL development, and production rollouts using Snowflake, AWS, cloud databases, and CI/CD pipelines. Collaborate across teams in a fast-paced financial services environment.

Design, build, and maintain the company data warehouse and ETL processes. Partner with business teams to define data solutions and visualizations, support reporting (SSRS, Crystal Reports) and BI (Power BI/Tableau), troubleshoot issues, and provide occasional after-hours support.

Senior consulting data engineer responsible for leading complex data engineering projects, coding robust solutions, designing complex data models, troubleshooting and optimizing data operations, mentoring engineers, interfacing with customers and cross-functional teams, and ensuring quality through testing, code reviews, and performance tuning to deliver on-time, on-budget solutions.

Senior technical leader responsible for architecting scalable data platforms and pipelines to support semantic and hybrid search, personalization, and GenAI use cases. Leads implementation of OLAP/OLTP, streaming, vector/graph-based search, entity enrichment, and production ML integration while managing offshore engineering teams and ensuring data governance, observability, and cross-functional alignment.

Design, build, optimize, and maintain scalable data pipelines and ETL for insights products. Analyze complex datasets, collaborate across teams, automate workflows, and improve pipeline performance and cost efficiency using cloud and modern data platforms.

Design, build, and maintain scalable data pipelines, lakes, and infrastructure using Databricks, Spark, Python, and AWS. Collaborate with ML engineers, data scientists, and analysts to deliver data products, ensure data quality and governance, implement DevOps/SDLC practices, mentor junior engineers, and evaluate new data technologies.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Design, build, and maintain reliable data pipelines and canonical datasets using dbt, Dagster, and Redshift. Partner with stakeholders to translate business questions into data models, dashboards, and self-service tools. Build BI dashboards (Hex/Tableau), use AI tooling to accelerate work, and collaborate with analysts and data scientists to ensure high-quality analytics infrastructure.

Design, build, and maintain scalable data pipelines and architectures. Perform data extraction, cleansing, transformation, and ensure data quality and accessibility. Collaborate with data scientists, analysts, and product teams to deliver data solutions and troubleshoot data issues.

Build, maintain, and troubleshoot data pipelines moving data into Snowflake. Write dbt models with tests/docs, backfill and validate data, investigate upstream data issues, run incident response for MSSQL/Snowflake/S3, and document processes.

Lead design and delivery of enterprise BI and data engineering solutions using medallion architecture and domain-driven design. Build ETL/ELT pipelines, advanced data models (star/snowflake), and Power BI dashboards. Implement DAX and Power Query M transformations, optimize SQL solutions, and use Spark Python for large-scale processing. Collaborate with stakeholders, define KPIs, mentor team members, and drive innovation across the Microsoft Cloud Data Stack.

Design, build, and maintain scalable data pipelines and ingestion frameworks across cloud platforms. Ensure data quality, lineage, storage, and consumption methods. Implement solutions on Kubernetes, Databricks, and Teradata, mentor junior engineers, collaborate with cross-functional teams, and contribute to internal tooling to support analytics and customer-facing data products.

Lead data engineering efforts within Technology Consulting: design data architecture and pipelines, implement AWS/Redshift and ETL solutions, support BI (QlikView/Oracle BI), coach teams, manage client relationships and SLAs, apply systems thinking to optimize outcomes and validate solutions with stakeholders.

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