Top Data & Analytics Jobs

The Data Engineer supports the data and analytics lifecycle by designing, developing, testing, and maintaining Big Data applications and pipelines. Responsibilities include building Spark and Scala processing logic, optimizing Hive and SQL queries, and maintaining integrations with HBase, Solr, JanusGraph, Kafka, and downstream systems. The role collaborates across business and technology teams, supports solution design and estimation, and develops skills through mentorship.

Designs, develops, and maintains cloud-native Azure data solutions, including data lakes, data marts, pipelines, ETL/ELT processes, and data transformation workflows. Integrates diverse data sources using Azure services, Spark, Python, R, and SQL. Applies Medallion architecture, CI/CD, Agile, DevOps, secure coding, authentication, monitoring, and data quality practices in a containerized environment.

Build and maintain AI billing, metering, consumption, and allocation data models. Reconcile usage and billing data across vendors, ensure data quality, and develop dashboards showing costs, utilization, trends, variances, and budget performance. Investigate anomalies and optimization opportunities while partnering with technology, finance, and business stakeholders on forecasting, governance, chargeback, cost transparency, and AI investment decisions.

Designs, builds, maintains, monitors, and optimizes cloud or on-premises ETL/ELT pipelines for large-scale batch and real-time data processing. Ensures data quality, security, compliance, reliability, scalability, and performance. Collaborates with business, technology, operations, governance, data quality, architecture, data science, DevOps, and BI teams to deliver production-grade data solutions.

Support the Global Client Group with sales and pipeline analytics, dashboards, forecasting, and executive-ready reporting. Deliver data-driven insights, develop BI tools, and drive process improvements using AI, automation, and cross-functional collaboration to optimize sales and business performance.

Designs and deploys scalable conceptual, logical, physical, and dimensional data models across enterprise Databricks Lakehouse and cloud environments. Responsibilities include creating star and snowflake schemas, data dictionaries, ER diagrams, metadata assets, profiling and validating data, analyzing lineage, and supporting data governance and quality. The role partners with business, technology, architecture, data science, DevOps, and BI teams on complex enterprise initiatives and mentors project teams.

Design and build end-to-end Azure data platforms (Synapse, Databricks, ADLS Gen2). Lead migrations, develop scalable PySpark/Spark SQL pipelines with ADF, enforce data engineering best practices (SCD, CDC, UPSERT/MERGE), optimize performance and costs, and mentor engineers while promoting CI/CD and AI-assisted tooling.

Design, implement, and maintain automated data workflows, ETL processes, and API integrations to improve data accessibility and reliability. Collaborate with architects, DBAs, developers, and business partners to support a centralized data warehouse, create documentation, troubleshoot integrations, and enable real-time reporting and analytics.

Translate business needs into functional requirements; analyze and validate data; design and deliver scalable data warehouses, marts, and lakes; collaborate with architecture, engineering, governance, and business teams; lead solution design and estimations; provide people leadership and coach talent; support global/onshore-offshore delivery and ensure data quality, documentation, and enterprise standards.

Lead design, build, troubleshoot, and deliver enterprise-scale data solutions on Azure and Hadoop. Provide technical oversight, mentoring, enforce standards, code, unit test, and collaborate with cross-functional partners to implement scalable data pipelines and analytics platforms.