Senior Data Architect

• Architect and govern enterprise Big Data platforms (data lake, lakehouse, warehouse, real-time).
• Design high-volume, high-velocity data pipelines using batch and streaming frameworks.
• Responsible for designing and optimizing Snowflake data models, ensuring scalability, performance, and cost efficiency across environments. 
• Lead end‑to‑end architecture for data ingestion, transformation, and consumption using Snowflake best practices, including micro‑partitioning, clustering, and query performance tuning. 
• Partner with cross‑functional teams to build secure and governed data solutions leveraging Snowflake features such as RBAC, data masking, dynamic tables, and streams/tasks. 
• Drive standardization of data architecture patterns, reusable frameworks, and coding practices across the data engineering team. 

• Evaluate and implement new Snowflake capabilities and integrations to enhance platform reliability and analytics maturity.
• Lead implementation of distributed processing architectures (Spark, PySpark, EMR).
• Build event-driven and real-time streaming solutions (Kafka, Kinesis, Flink).
• Define ETL/ELT patterns, metadata-driven pipelines, and reusable ingestion frameworks.
• Drive data platform automation (Airflow/Step Functions, CI/CD, data quality, observability).
• Optimize performance, scalability, fault tolerance, and cost across Big Data workloads.
• Integrate GenAI architectures (LLMs, embeddings, vector databases, RAG) with enterprise data lakes.
• Ensure security, governance, lineage, and compliance across data platforms.
• Provide hands-on leadership and technical mentoring to data engineering teams.

• 8-10 years in Big Data Engineering / Data Architecture roles.
• Expert-level experience with Spark, PySpark, SQL, and distributed compute engines.
• Strong knowledge of AWS Big Data stack: S3, EMR, Glue, Athena, Redshift, Lambda, Step Functions.
• Hands-on experience with Snowflake (performance tuning, data sharing, optimization).
• Expertise in streaming platforms: Kafka, Kinesis, Flink, or Spark Streaming.
• Strong experience with data modeling (dimensional, Data Vault 2.0).
• Proficiency in Python, schema evolution, partitioning, and data versioning.
• Experience with orchestration and automation tools (Airflow, Dagster, CI/CD).
• Experience with Agile delivery and leading globally distributed engineering teams.

 

 

*the base salary for this position is $150,000-$175,000

EXL (NASDAQ: EXLS) is a leading data analytics and digital operations and solutions company. We partner with clients using a data and AI-led approach to reinvent business models, drive better business outcomes and unlock growth with speed. EXL harnesses the power of data, analytics, AI, and deep industry knowledge to transform operations for the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media and retail, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect. We are headquartered in New York and have more than 54,000 employees spanning six continents. For more information, visit www.exlservice.com.

EXL never requires or asks for fees/payments or credit card or bank details during any phase of the recruitment or hiring process and has not authorized any agencies or partners to collect any fee or payment from prospective candidates. EXL will only extend a job offer after a candidate has gone through a formal interview process with members of EXL’s Human Resources team, as well as our hiring managers.