Data Engineer

Data Engineer

Hands-on experience in building robust metadata-driven, automated data pipeline solutions leveraging modern cloud-based data technologies, tools for large data platforms.

• Hands-on experience leveraging data security, governance methodologies meeting data compliance requirements.

• Experience building automated ELT data pipelines, snowpipe frameworks leveraging Qlik Replicate, DBT Cloud, snowflake with CICD.

• Hands-on experience building data integrity solutions across multiple data sources and targets like SQL Server, Oracle, Mainframe-DB2, files, Snowflake.

• Experience working with various structured & semi-structured data files - CSV, fixed width, JSON, XML, Excel, and mainframe VSAM.

• Experience using S3, Lambda, SQS, SNS, Glue, RDS AWS services.

• Proficiency in Python, Pyspark, advanced SQL for ingestion frameworks and automation.

• Hands-on data orchestration experience using DBT cloud, Astronomer Airflow.

• Experience in implementing logging, monitoring, alerting, observability, performance tuning techniques.

• Implement and maintain sensitive data protection strategies – tokenization, snowflake data masking policies, dynamic & conditional masking, and role based masking rules.

• Strong experience designing, implementing RBAC, data access controls, adopting governance standards across Snowflake and supporting systems.

• Strong experience in adopting release management guidelines, code deployment to various environments, implementing disaster recovery strategies, leading production activities.

• Experience implementing schema drift detection and schema evolution patterns.

• Must have one or more certifications in the relevant technology fields.

• Nice to have Financial banking experience.

Roles & Responsibilities

• Data Pipelines - Design, develop, and maintain data pipelines for efficient data processing and integration for real-time and batch-processing. Implement and optimize ETL processes to extract, load, transform and integrate data from various sources. Enhance data flows and storage solutions for improved performance

• Data Warehousing - Design and implement data warehouse structures. Ensure data quality and consistency within the data warehouse. Apply data modeling techniques for efficient data storage and retrieval

• Data Governance, Quality & Compliance - Implement data governance policies and procedures. Implement data quality frameworks, standards and documentation. Ensure compliance with relevant data regulations and standards

• Security & Access Management - Implement data security measures and access controls. Maintain data protection protocols

• Performance Optimization and Troubleshooting - Analyze and optimize system performance for large-scale data operations. Troubleshoot data issues and implement robust solutions

• Testing & Automation – Write unit test cases, validate the data integrity & consistency requirements, adopt automated data pipelines using GitLab, Github, CICD tools.

• Code Deployment & Release Management - Adopt release management processes to promote code deployment to various environments including production, disaster recovery, and support activities.

• Cross-Functional Collaboration - Collaborate with data scientists, analysts, and other teams to understand and meet data requirements. Participate in cross-functional projects to support data-driven initiatives