Data Engineer Big

Ram

*********@*****.*** +1-289-***-****

Professional Summary

Experienced Data Engineer with 6+ years in building scalable data pipelines, data warehouses, and analytics solutions across

banking, consulting, and SaaS domains. Skilled in big data frameworks, cloud platforms (AWS, Azure), SQL/NoSQL

databases, and ETL tools. Adept at partnering with business stakeholders to deliver secure, reliable, and high-performing

data solutions supporting real-time analytics and BI initiatives.

Technical Skills

Programming: Python, SQL, PySpark, Scala

Data Engineering: ETL/ELT, Data Pipelines, Workflow Orchestration (Airflow, ADF, Step Functions)

Big Data Frameworks: Apache Spark, Hadoop, Kafka

Databases/Warehouses: Snowflake, Redshift, Azure Synapse, SQL Server, PostgreSQL, MongoDB

Cloud Platforms: AWS (S3, Redshift, Glue, EMR, Lambda), Azure (Data Factory, Synapse, Databricks, ADLS)

DevOps & Tools: Git, Jenkins, Terraform, Docker, Kubernetes

Visualization & BI: Power BI, Tableau

Methodologies: Agile/Scrum, CI/CD, DataOps

Professional Experience

Client: BMO, Toronto, ON

Role: Senior Data Engineer

Mar 2024 – Present

Project: Enterprise Data Lake Modernization

Description: Building a next-generation AWS-based Enterprise Data Lake to integrate structured, semi-structured, and

streaming data sources for regulatory compliance, customer insights, and fraud detection across BMO’s retail and

commercial banking divisions.

Responsibilities:

Design and develop scalable data pipelines using AWS Glue, PySpark, and Lambda for ingestion from core banking

systems.

Build data lake architecture on Amazon S3, integrating with Redshift for advanced analytics and BI reporting.

Implement real-time data streaming solutions using Kafka + Kinesis for fraud detection and transaction

monitoring.

Optimize query performance on Redshift using partitioning, clustering, and distribution keys.

Work closely with compliance teams to ensure data governance and regulatory requirements (CCAR, Basel III)

are met.

Apply CI/CD with Jenkins and Terraform for infrastructure provisioning and deployment automation.

Collaborate with data scientists to provide clean, reliable datasets for predictive risk models.

Client: Tiger Analytics, Ottawa, ON

Role: Data Engineer

Jan 2022 – Feb 2024

Project: Customer 360 Platform – Azure Cloud Migration

Description: Migrated and enhanced a Customer 360 platform for a leading North American retailer, consolidating customer

interactions, sales, and marketing data into a unified platform for personalization and customer segmentation.

Responsibilities:

Developed ETL pipelines in Azure Data Factory to migrate data from on-premise SQL Server and Oracle into Azure

Data Lake (ADLS).

Implemented data transformations in Azure Databricks using PySpark for cleansing, standardization, and

enrichment.

Designed star schema models in Azure Synapse to support BI dashboards and reporting.

Built reusable data ingestion frameworks reducing processing time by 30%.

Applied data quality checks and monitoring frameworks using PySpark and Delta Lake.

Collaborated with data analysts and business teams to deliver customer churn and recommendation models.

Ensured compliance with GDPR and data privacy regulations during cloud migration.

Client: Clio, Toronto, ON

Role: Associate Data Engineer

Apr 2019 – Dec 2021

Project: Legal SaaS Data Platform

Description: Developed a data analytics platform for Clio’s legal practice management SaaS product, enabling law firms to

gain insights on case management, billing, and client interactions.

Responsibilities:

Designed and implemented ETL pipelines to load application and customer usage data into a centralized warehouse.

Utilized Python and SQL to automate data extraction and transformation from transactional databases.

Deployed data pipelines in Airflow for scheduling and monitoring batch workflows.

Supported migration from legacy reporting systems to Snowflake-based data warehouse.

Partnered with product managers to deliver usage analytics dashboards in Power BI.

Improved data processing performance by optimizing SQL queries and indexes.

Contributed to establishing data governance practices ensuring accuracy and consistency of KPIs.

Contact this candidate