Top Data & Analytics Jobs

Design, build, and operate scalable batch and streaming data pipelines and a lakehouse on AWS. Implement ETL/ELT, CDC, metadata/catalog and governance, automated AWS provisioning (IaC), CI/CD for data pipelines, data quality and lineage, observability, and documentation while collaborating across teams.

Designs and executes end-to-end SAP S/4HANA data migrations: extract, transform, validate, reconcile, and cutover. Configures LTMC, SLT, and LVM, optimizes large-volume loads, creates validation/reconciliation reports, supports mock migrations and cutover, and maintains technical documentation while ensuring data governance and compliance.

Design, build, and operate large-scale Hadoop-based data pipelines and streaming systems. Implement ETL/ELT with Spark, Hive, Pig, and Sqoop; build Kafka/Spark Streaming/Flink pipelines; optimize performance; enforce governance, monitoring, and SLAs; automate orchestration with Airflow/Oozie; collaborate with analysts and data scientists; document architectures and mentor junior engineers.

Own day-to-day Snowflake administration and the Sigma semantic layer; manage ingestion (Fivetran), dbt modeling scope, reverse-ETL to Salesforce/HubSpot/NetSuite, data quality SLAs, reporting and dashboards, partner with managed data vendors, design automation and reproducible month-end financial analytics, and drive analysis and experiments for AdOps, Sales, and Finance.

Own and operate the Snowflake warehouse and Sigma semantic layer; manage ingestion (Fivetran), dbt model scoping and review, reverse-ETL, data quality SLAs, dashboards, month-end financial analytics, vendor management, and automation to support AdOps, Sales, Finance, and executive reporting.

The Sr. Data Engineer designs ETL solutions and data architecture, collaborates on technical solutions, and addresses data management functions.

Develop and support analytics solutions by extracting and prepping data, building data models, dashboards, and integrated analytics. Partner with business and IT to design EDW/Data Lake, ensure data quality and governance, automate monitoring, and introduce predictive/data science elements. Promote BI best practices and sustain user-facing analytics performance.

The Mid-Level Data Engineer will design and optimize data pipelines and services, supporting analytics and machine learning readiness in an Azure environment.

Lead technical design and implementation of production-grade data platforms, ETL/ELT and streaming pipelines, containerized Python data applications, Snowflake and dbt modeling, observability, CI/CD, and AI/ML data integrations to enable scalable data products and business-aligned analytics.

Lead architecture and execution of Faire's data platform and analytics capabilities. Build and operate scalable, secure ETL/ELT, streaming, storage, compute, orchestration, and data warehousing at petabyte scale. Drive performance, cost, monitoring, governance, and self-serve analytics; influence cross-functional teams and mentor engineers.

The Data Engineer will manage production data quality tasks, support daily data loads, build ETL pipelines, and coordinate with teams to resolve data issues in an Azure Cloud-based environment.

As a Data Engineer, you will develop solutions for customer-centric technology, ensuring platform integration and managing data architecture. Responsibilities include building data processes, maintaining data integrity, and collaborating cross-functionally to meet business challenges.

The Principal Data Engineer will lead technical direction, manage data platforms, make architectural decisions, mentor engineers, and ensure data systems operate reliably.

The DMS Data Engineer is responsible for analyzing raw data, building data systems and pipelines, conducting complex data analyses, and collaborating on projects to enhance data quality and reliability.

Lead and mentor multiple Databricks-focused data engineering teams, define engineering best practices and reusable frameworks, drive CI/CD and performance/cost optimization, build and support analytical and streaming pipelines, and collaborate with architects, product owners, and cross-functional partners to deliver high-quality data products.

The Data Engineer Consultant will design and implement scalable data solutions, advise clients on optimizing data environments, and develop ETL pipelines and data models to support business objectives.

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 and build scalable cloud-based Azure data solutions, implement ETL and integration, ensure data quality and interoperability, support reporting and analytics, mentor junior engineers, and follow Agile/DevOps practices to deliver production-ready solutions for healthcare use cases.

Design, implement, and maintain automated ingestion and transformation workflows to bring spreadsheet-based operational data (JSON/CSV/Excel) into enterprise databases and Pega workflows. Validate data integrity, troubleshoot pipelines, support reporting integrations, document data flows, and collaborate with architects, developers, and stakeholders to modernize and secure enterprise data services.

Design and build production-grade data pipelines on Databricks and AWS extracting from enterprise ERPs, implement medallion architectures, ensure data quality/governance, collaborate with business SMEs, and deliver AI-ready data products.

The Software Engineer will manage data collection for AI model training, enhance the ingestion pipeline on GCP, and collaborate with the AI team to improve dataset quality and efficiency.

Lead architecture and evolution of enterprise-scale data platforms and pipelines for batch, real-time, and event-driven use cases. Drive data modeling, performance tuning, data quality, CI/CD, observability, incident response, and cross-team technical leadership. Mentor engineers, lead design reviews, and align data strategy across hybrid environments.

Own and scale streaming Kafka pipelines and large-scale Spark batch jobs processing billions of rows. Drive pipeline reliability, observability, data modeling, and cost/latency optimizations. Partner with data scientists and platform engineers, define SLOs, improve efficiency, contribute to shared infrastructure (Airflow, libraries, data catalog), and mentor junior engineers.

Design, build, and operate large-scale Hadoop-based data pipelines and streaming systems. Implement ETL/ELT, optimize Spark/MapReduce jobs, manage storage formats and governance, automate orchestration, monitor SLAs, collaborate with data scientists, document runbooks, and mentor junior engineers.

Lead design and delivery of scalable ETL/ELT pipelines into Snowflake, manage AWS data infrastructure, build Spark transformations in Databricks using Python/PySpark, and implement a semantic layer and modular dbt models for consistent, governed business metrics.