Top Data & Analytics Jobs

Design and build end-to-end data pipelines, integrations, and APIs; maintain the data warehouse and ensure data accuracy, reliability, and security. Partner with cross-functional teams to translate needs into scalable solutions, mentor engineers, provide operational support, document integrations, and contribute to governance and enterprise architecture.

Design, build, and maintain scalable data pipelines and ETL/ELT workflows ingesting upstream production and operational data. Architect data integrations with SCADA/ERP sources, ensure data quality, performance monitoring, capacity planning, disaster recovery, documentation, and mentor junior engineers while aligning data engineering priorities with business objectives.

Design, build, and operate large-scale data pipelines and ETL processes to normalize varied partner datasets. Lead technical decisions, mentor engineers, implement testing and monitoring, manage data warehouses/lakes, and collaborate cross-functionally to deliver robust data infrastructure that supports ML and product teams.

Build and maintain scalable pipelines to ingest, clean, and integrate large multimodal datasets. Own ingestion across APIs, files, cloud storage, and warehouses. Design linkage, entity resolution, deduplication, and schema harmonization. Implement data quality checks, validation, and documentation. Collaborate with engineering, research, and deployment teams to prepare integrated data for simulations and evaluate new data sources.

Senior technical leader embedded in a Scrum team to design, build, and maintain scalable batch and real-time data pipelines and services; lead data modeling and warehousing; enforce data engineering best practices, quality frameworks, SLAs/SLOs, and observability; translate product and analytics needs into durable data platforms; mentor engineers and influence cross-team technical direction.

Senior Data Engineer designs, builds, and maintains scalable cloud data pipelines and platforms (Snowflake/Databricks/Microsoft Fabric), implements ELT/ETL, optimizes data models, supports BI/AI use cases, participates in solution design and client engagements, mentors junior engineers, and ensures data products are documented and production-ready.

Design, build, and optimize the company lakehouse environment and ETL/ELT pipelines (Databricks/Azure/Fabric). Develop semantic models, governed metrics, data quality controls, monitoring, and CI/CD practices. Partner with business, security, and analytics teams to steward master data, implement governance and access controls, and enable reliable datasets for reporting and AI use cases.

Design, build, and operate scalable data transformation pipelines and dimensional models using dbt, SQL, and AWS. Implement infrastructure-as-code (AWS CDK), CI/CD, and query performance optimizations while ensuring data quality, governance, and collaboration with cross-functional and offshore teams.

Lead Data Engineer builds and maintains scalable data pipelines and lakehouse/warehouse platforms (Databricks) to support analytics, BI, reporting, and AI. Responsibilities include data modeling, ETL/ELT, medallion architecture, orchestration and monitoring (Airflow/Cron), performance tuning, CDC/incremental loads, data quality, and leading/mentoring a team while collaborating with stakeholders and maintaining documentation.

Identify, evaluate, and integrate external data sources to enhance the platform. Build and maintain automated ETL pipelines, ensure data quality and governance, document workflows, perform cost-benefit analyses of vendors/tools, and present recommendations to leadership while collaborating with product and engineering teams.

Design, build, and maintain reliable data pipelines and data warehouse artifacts. Collaborate with engineering, product, and research teams to prototype solutions, document data products, and ensure quality and scalability.

Lead design and delivery of Snowflake-based data solutions, data models, ETL pipelines and data warehouse applications. Provide architecture direction, drive technology transformation and POCs, evangelize modern data architecture, collaborate with global stakeholders and Agile teams, and apply advanced analytics (ML/AI/GenAI) while ensuring data security and PII management.

Design, build, and maintain reliable, efficient data pipelines and infrastructure; guide data architecture and proofs-of-concept; collaborate with engineering, product, and research to develop data products; document data warehouse and data products; write, QA, and maintain data engineering code and provide technical leadership on projects.

Design, build, and maintain scalable ETL pipelines and data integration solutions using SQL Server, SSIS, and Azure Data Factory. Lead logical and physical data modeling, create data marts and operational stores, ensure data quality and governance, optimize ETL performance, and collaborate with stakeholders to translate business needs into enterprise data solutions.

Technical staff-level data engineer who sets direction for ingestion, storage, and search systems. Designs scalable, reliable pipelines and storage, establishes architecture and observability standards, mentors engineers, leads complex multi-quarter initiatives, participates in on-call, and partners with AI/ML and product teams to deliver queryable, high-volume telemetry for analytics and detection.

Design, build, and optimize scalable cloud data pipelines and infrastructure. Maintain and refactor SQL and ETL processes, automate data workflows, ensure data quality/security, and troubleshoot complex issues. Provide technical leadership and mentor junior engineers while collaborating with stakeholders to translate requirements.

Designs and supports data platform solutions, solves complex problems, and applies best practices across teams. Exercises judgment using multiple information sources, mentors junior colleagues, and may lead small projects. Collaborates across groups to integrate systems and clearly communicate difficult or sensitive technical information.

Design, implement, and maintain data quality processes using cloud platforms (Azure/AWS/GCP). Collaborate with stakeholders to define standards, perform data profiling and cleansing, document issues and remediation, generate quality metrics, and support cross-functional teams to improve data architectures and integration.

Design, implement, and maintain PySpark-based data reconciliation solutions for financial systems. Build matching algorithms, integrate with rules engines, process large distributed datasets, identify and resolve discrepancies, and collaborate with analysts and architects to improve data quality and governance.

Design, build, and optimize PySpark applications and ETL pipelines to process large-scale datasets from SQL/NoSQL sources, data lakes, and streaming platforms. Ensure data quality, error handling, performance tuning, and collaborate with analysts, scientists, and architects to deliver scalable data solutions.

Design, implement, and maintain PySpark applications to automate large-scale financial data reconciliations. Build transformation and matching algorithms, integrate with rules engines, analyze data gaps, and collaborate with analysts and architects to ensure data quality and system resilience.

Design, build, and maintain scalable ETL/ELT pipelines into Snowflake, enforce data quality, optimize data models and query performance for Power BI reporting, produce documentation, and troubleshoot data issues while collaborating with stakeholders.

Design, develop, and maintain PySpark applications and ETL pipelines to process, transform, and integrate large-scale datasets from SQL, NoSQL, data lakes, and streaming sources. Optimize Spark job performance, implement robust error handling, and collaborate with data analysts, scientists, and architects using orchestration tools like Airflow or Luigi.

Design, develop, test, and deploy high-performance Spark/Scala data processing applications and ETL pipelines using the Cloudera Hadoop ecosystem. Optimize Spark and platform performance, ensure data integrity and security, collaborate with data scientists and analysts, troubleshoot issues, and implement version control and CI/CD for Spark applications.

Design and develop Big Data applications using Java, Spark, and MapReduce on Cloudera/Hadoop ecosystems. Work with Hive, Impala, YARN, Kafka, and large datasets; write tests (JUnit), perform data analysis, and script in Unix/Python. Lead and manage global technology teams and follow industry best practices.