Manohar Venkatesh

Formulated and executed comprehensive data architecture strategies across the enterprise to
accommodate complex business systems and evolving analytical requirements.
Created scalable and efficient data models and databases that align with business needs and
adhere to industry best practices.
Implemented and upheld data governance policies and procedures to guarantee data quality,
consistency, and security throughout the organization.
Directed a team of data engineers and analysts to successfully carry out data management and
integration projects.
Realized a 20% enhancement in data processing efficiency by refining ETL workflows, indexing
techniques, and query performance.
Engineered and established real-time data pipelines for the ingestion and processing of
transactional data into Azure Data Lake.
Developed a data warehouse using Azure Synapse Analytics to aid in risk management and fraud
detection efforts.
Constructed dimensional models utilizing star and snowflake schemas following the Kimball
methodology to facilitate analytical reporting.
Employed tools such as Erwin Data Modeler and Azure Data Studio for both logical and physical
data modeling.
Created and maintained fact and dimension tables to support essential business metrics and
enhance query performance.
Performed schema-level data validation and profiling to support data quality initiatives
Mentored junior engineers and led knowledge-sharing initiatives to elevate team capabilities

Transformed complex business requirements into scalable, high-performance data architectures
by collaborating with cross-functional teams across enterprise environments.
Designed and implemented robust data integration frameworks to unify disparate data sources
into centralized, analytics-ready platforms, enhancing data accessibility and consistency.
Developed interactive dashboards and visualization tools that enabled real-time, data-driven
decision-making across multiple business units.
Established automated data quality monitoring systems to ensure data accuracy, consistency,
and compliance with regulatory standards across all data pipelines.
Led successful migrations of legacy data infrastructures to modern cloud-based platforms
(Azure), ensuring zero data loss and minimal operational disruption.
Engineered distributed data processing pipelines using Apache Spark on Amazon EMR to efficiently
manage and analyze large-scale datasets.
Processed semi-structured and unstructured data formats (JSON, Avro, Parquet) with advanced
transformation logic using Scala.
Built a Spark-based schema validation framework to enforce rigorous data quality checks during
ingestion and transformation stage
Architected data solutions using Oracle Financial Services Analytical Applications (OFSAAI) to
support financial risk modeling, regulatory reporting, and capital adequacy assessments.
Integrated KYC and AML tools into enterprise data workflows to detect suspicious activities,
enhance transparency, and ensure compliance with global financial regulations.
Collaborated with Enterprise Case Management (ECM) systems to streamline investigative
workflows, manage escalations, and support audit readiness.

Proficient in IBM DataStage for designing, developing, and deploying complex ETL workflows.
Extensive experience in optimizing Spark jobs using combiners, partitioning, and distributed cache
for high-performance data processing.
Processed over 30 TB of structured and unstructured data into HDFS from various sources.
Created Hive external tables with partitioning and implemented optimized joins for efficient
querying and reporting.
Developed advanced Alteryx analytic applications for modeling Return on Advertising Spend using
Alteryx, R, and Tableau.
Built and maintained a report generation framework to support business intelligence initiatives.
Applied regression and time series models for predictive analytics and trend forecasting.
Designed and implemented streaming data pipelines in Alteryx for real-time data processing.
Performed data ingestion and extraction using Amazon S3 for cloud-based data workflows.
Gathered business requirements and translated them into scalable technical specifications.
Developed Spark applications and deployed them on Amazon EMR for large-scale distributed
processing.
Handled semi-structured and unstructured data formats (XML, JSON, Avro, Parquet) using Scala.
Implemented data quality frameworks and schema validations using Spark.
Developed Sqoop jobs for efficient data ingestion into HDFS and Hive.
Executed Spark RDD transformations and actions to support complex business analysis.

Worked within various SDLC methodologies including Waterfall, Agile, and Scrum to deliver data
integration solutions.
Acted as the single point of contact for the client from the development team, ensuring clear
communication and timely delivery.
Conducted peer reviews of SQL scripts, Control-M job schedules, and DataStage ETL jobs to
maintain code quality and consistency.
Collaborated with business and functional users to gather requirements for development and
enhancement tasks.
Coordinated with multiple teams to manage data inflow and outflow processes for assigned
projects.
Performed data integration for enterprise data warehouse applications, ensuring data consistency
and reliability.
Designed and implemented star schema data models to support analytical and reporting needs.
Developed and maintained DataStage jobs for ETL processes, supporting both batch and real
time data workflows.