Summary
✨ AI‑Generated
A senior data engineering role focused on designing and managing scalable cloud-based data platforms and pipelines. You will build and optimize ETL/ELT frameworks, develop modern data architectures, create data models and marts, and support analytics, reporting, AI, and operational use cases. The role also involves consulting with stakeholders, translating business requirements into technical solutions, and leading engineering teams.
Highlights
Opportunity to lead modern cloud data engineering initiatives, design scalable data platforms and pipelines, work closely with client stakeholders, and guide teams delivering high-impact analytics and AI solutions.
Description
EXL is seeking a Senior Data Engineer to join our Data & Analytics practice and support strategic client engagements.
This role will be responsible for designing, building, and managing scalable cloud-based data platforms, driving modern data engineering practices, and leading teams delivering high-impact data solutions.
The ideal candidate will combine strong technical expertise with consulting and leadership capabilities, partnering closely with client stakeholders to translate business requirements into scalable data architectures and engineering solutions.
Responsibilities:
Design, develop, and maintain scalable data pipelines and data products supporting analytics, reporting, AI, and operational use cases.Build and optimize ETL/ELT frameworks for large-scale data ingestion, transformation, validation, and consumption.Develop and manage cloud-native data platforms leveraging Snowflake, AWS, Apache Airflow and modern data architectures.Create scalable data models, data marts, semantic layers, and curated datasets that support enterprise analytics initiatives.Optimize SQL workloads, transformation logic, and query performance to improve scalability and cost efficiency.Establish reusable engineering frameworks, accelerators, and best practices to improve delivery consistency across projects.Ensure high standards of data quality, reliability, governance, and observability throughout the data lifecycle.Develop and maintain Snowflake-based data ecosystems, leveraging advanced features for performance optimization and data sharing.
Required Qualifications
4+ years of experience in Data Engineering, Big Data Engineering, or Cloud Data Platform development.Bachelor's or Master's degree in Computer Science, Engineering, Analytics, Mathematics, Information Systems, or related disciplines.Strong hands-on expertise in SQL, Python, and PySpark.Extensive experience working with Snowflake, Databricks, or similar cloud-native data platforms.Proven experience building and supporting large-scale ETL/ELT data pipelines.Strong understanding of data warehousing concepts, dimensional modeling, and modern Lakehouse architectures.Experience implementing Medallion Architecture and enterprise-grade data modeling practices.Hands-on experience with workflow orchestration tools such as Apache Airflow or equivalent scheduling frameworks.Experience working with cloud ecosystems including AWS, Azure, or GCP.Strong knowledge of performance tuning, optimization, monitoring, and operational support for data platforms.
Preferred Qualifications
Experience with streaming and real-time data processing frameworks.Familiarity with DataOps, CI/CD, Infrastructure as Code, and DevOps practices.Experience with data governance, data quality frameworks, and metadata management.Exposure to AI/ML data pipelines and feature engineering workflows.Experience with visualization tools such as Tableau, Power BI, or Looker.Hands-on experience with Big Data technologies including Spark, Hadoop, Hive, HBase, Kafka, or related platforms.Consulting or client-facing delivery experience in enterprise-scale environments.
