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Only 24h Left Lead Assistant Manager

4 weeks ago

Gurgaon, Haryana, India EXL Full time
Job Description

Job Title: Data Engineer

Job Summary

Data Engineers will be responsible for the design, development, testing, maintenance, and support data assets including; Azure Data Lake and data warehouse development, modeling, package creation, SQL script creation, stored procedure development, integration services support among other responsibilities.

Candidate have at least 3-5 years hands-on AWS experience as a Data Engineer, must be an expert in SQL and have extensive expertise building data pipelines. Candidate will be accountable for meeting deliverable commitments including schedule and quality compliance. This Candidate must have skills to plan and schedule own work activities, coordinate activities with other cross-functional team members to meet project goals.

Basic Understanding Of

- Scheduling and workflow management & working experience in either ADF, Informatica, Airflow or Similar
- Enterprise Data Modelling and Semantic Modelling & working experience in ERwin, ER/Studio, PowerDesigner or Similar
- Logical/Physical model on Big Data sets or modern data warehouse & working experience in ERwin, ER/Studio, PowerDesigner or Similar
- Agile Process (Scrum cadences, Roles, deliverables) & basic understanding in either Azure DevOps, JIRA or Similar
- Architecture and data modelling for Data Lake on cloud & working experience in Amazon WebServices (AWS), Microsoft Azure, Google Cloud Platform (GCP)
- Basic understanding of Build and Release management & working experience in DevOps, AWS CodeCommitt or Similar

Strong In

- Writing code in programming language & working experience in Python, PySpakrk, Scala or SimilarData Pipeline Development & Management
- Design, develop, and maintain ETL (Extract, Transform, Load) pipelines using AWS services like AWS Glue, AWS Data Pipeline, Lambda, and Step Functions.
- Optimize and monitor data pipelines for performance, cost, and scalability.
- Implement incremental data processing using tools like Apache Spark (EMR), Kinesis, and Kafka.
- Work with AWS data storage solutions such as Amazon S3, Redshift, RDS, DynamoDB, and Aurora.
- Optimize data partitioning, compression, and indexing for efficient querying and cost optimization.
- Implement data lake architecture using AWS Lake Formation & Glue Catalog.
- Develop real-time and batch data ingestion using AWS services like Kinesis, Kafka, and Glue.
- Integrate data from multiple sources (databases, APIs, IoT, logs, third-party sources, etc.).
- Ensure low-latency and high-throughput data ingestion.
- Implement big data processing using AWS EMR (Hadoop/Spark), AWS Glue (PySpark), and AWS Lambda.
- Write complex SQL queries and transformations in Redshift, Athena, and Presto.
- Optimize Spark jobs and query performance tuning.
- Implement IAM roles, policies, and permissions for securing AWS data resources.
- Set up encryption, logging, and auditing using AWS KMS, CloudTrail, and CloudWatch.
- Ensure compliance with GDPR, HIPAA, or other regulatory frameworks.
- Automate AWS infrastructure using Terraform, AWS CloudFormation, or CDK.
- Implement CI/CD pipelines for data workflows using CodePipeline, CodeBuild, and GitHub Actions.
- Use AWS Step Functions for orchestrating workflows.
- Monitor data workflows using CloudWatch, AWS X-Ray, and third-party tools (Datadog, Prometheus).
- Optimize compute and storage costs by using Spot Instances, Auto Scaling, and Reserved Instances.
- Fine-tune queries, indexes, and partitions for improved performance.
- Work closely with Data Scientists, Analysts, and Software Engineers to understand data requirements.
- Document data architecture, ETL processes, and best practices.
- Provide knowledge transfer and mentoring to junior engineers.