Lead Data Engineer at EXL

Job 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. Data Engineering & Platform Development • 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. • Build and orchestrate data workflows using Airflow and other workflow scheduling platforms. • Collaborate directly with client stakeholders to gather requirements, define roadmaps, and develop scalable technical solutions. • Present solution designs, technical recommendations, and project updates to both technical and business audiences. • Prepare and maintain comprehensive project documentation, technical specifications, architecture diagrams, and operational runbooks. 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. • Demonstrated experience leading engineering teams and coordinating with client and internal stakeholders. • Excellent analytical, problem-solving, communication, and stakeholder management skills. • Ability to work independently and lead complex initiatives in fast-paced consulting environments. 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. Position: Senior Data Engineer Location: Remote in Canada and US Salary: $110k-$120k USD base + bonus + benefits The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.

Requirements

Must-haves

Data Engineering & Platform Development
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.
Build and orchestrate data workflows using Airflow and other workflow scheduling platforms.
Collaborate directly with client stakeholders to gather requirements, define roadmaps, and develop scalable technical solutions.
Present solution designs, technical recommendations, and project updates to both technical and business audiences.
Prepare and maintain comprehensive project documentation, technical specifications, architecture diagrams, and operational runbooks.
4+ years of experience in Data Engineering, Big Data Engineering, or Cloud Data Platform development.

Nice-to-haves

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.
Position: Senior Data Engineer
Location: Remote in Canada and US
Salary: $110k-$120k USD base + bonus + benefits

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Keywords to mirror

aiawssqlpythonazuregcpci/cdml

Platform: oracle-cloud
Effort: medium