Senior Data Engineer

Our passion at EXL is enabling organizations to achieve a measurable, competitive advantage through Data Management. We partner with our clients to design and implement scalable, accessible, and innovative solutions. Our goal is to make sense of data to drive client’s business forward through data strategy, data governance and building cloud data platforms. EXL is a leading data analytics and digital operations and solutions company that partners with clients to improve business outcomes and unlock growth. By bringing together deep domain expertise with robust data, powerful analytics, cloud, artificial intelligence (“AI”) and machine learning (“ML”), we create agile, scalable solutions and execute complex operations for the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media, and retail, among others. Focused on driving faster decision-making and transforming operating models, EXL was founded on the core values of innovation, collaboration, excellence, integrity and respect. Headquarters in New York, our team is over 44,500+ strong, with more than 50 offices spanning six continents. Role Name: AWS Data & Solution Engineer Location: NYC, NY Employment Type: Full-Time Mode – Hybrid (2-3 days from Office) Job Summary Are you committed to building data solutions, and being a Data Management expert? Are you passionate about data? Would you like to work on solutions with tangible impact to our clients? We are looking for an Sr AWS Data & Solutions Engineer with primary skills on Python & PySpark development who will be able to design and build solutions for one of our Fortune 500 Client programs, which aims towards building an Enterprise Data Lake on AWS Cloud platform, build Data pipelines by developing several AWS Data Integration, Engineering & Analytics resources. You will be responsible for building API services using FastAPI or Flask frameworks. Key Responsibilities Design, build and unit test applications on Spark framework on Python. Build Python and PySpark based applications based on data in both Relational databases (e.g. Oracle), NoSQL databases (e.g. DynamoDB, MongoDB) and filesystems (e.g. S3, HDFS) Build AWS Lambda functions on Python runtime leveraging awswrangler, pandas, json, requests Build PySpark based data pipeline jobs on AWS Glue ETL or EMR Clusters Build Python based event-driven integration with Kafka Topics, leveraging Confluent libs Leveraged Apache Iceberg to manage schema evolution and ACID-compliant CDC merges within the data lakeDesign and Build API services using FastAPI, understand the swagger metadata files and implement OAuth2/JWT authentication for protected endpoints Build the process orchestration pipelines using AWS Step Functions and Eventbridge rules. Optimize performance for data access requirements by choosing the appropriate native Hadoop file formats (Avro, Parquet, ORC etc) and compression codec respectively. Deploy applications on Docker and Kubernetes containers Leverage copilot/GPT for agentic coding of above tech stack Optimize performance of Spark applications in Hadoop using configurations around Spark Context, Spark-SQL, Data Frame, and Pair RDD's Setup the Glue crawlers to catalog OracleDB tables, MongoDB collections and S3 objects Ability to monitor, troubleshoot and debug failures using AWS CloudWatch and Datadog Ability to solve complex data-driven scenarios and triage towards defects and production issues Participate in code release and production deployment. Create documentation for user adoption, deployments, runbook, and support client users for enablement or for any issues encountered. Perform code reviews with the team and enable them to develop code for complex scenarios Participate in the agile development process, and document and communicate issues and bugs relative to data standards in scrum meetings Work collaboratively with onsite and offshore team. Voice the opinions to multiple teams and thus driving the entire initiative with strong leadership Education & Experience Bachelor’s Degree or equivalent in computer science or related and minimum 10+ years of experience Certified on one of - Solution Architect, Data Engineer or Data Analytics Specialty by AWS Require hand-on experience on Python and PySpark programming Require hands-on experience on AWS S3, Glue ETL & Catalog, Lamba Functions, EventBridge, Step Functions, Athena Require hands-on experience on Kafka integrations Require hands-on experience on Python pandas, requests, boto3 module Require hands-on experience in writing complex SQL queries Require hands-on experience using REST APIs using FastAPI or Flask Require hands-on experience building Agentic AI workflows Preferred expertise on Snowflake, AWS Redshift & DynamoDB Ability to use AWS services, predict application issues and design proactive resolutions Require Technical Coordination skills to drive requirements and technical design Requires aptitude to help build skillset within organization Knowledge, Skills & Abilities Data pipelines using Python and PySpark on AWS Glue, EMR and lambda functions. Develop and secure RESTful APIs (FastAPI) on Docker/EKS containers and implement OAuth2/JWT authentication for protected endpoints Hands-on experience with Apache Iceberg tables for cdc and latest snapshots Event based pipelines for consuming/publishing to/from Apache Kafka/MSK Lead and communicate complex technical designs and leverage copilot/GPT for agentic coding of above tech stack #J-18808-Ljbffr