RUPESH MALLADI

• Built a central Data Engineering practice at Fortegra, unifying previously siloed efforts across operations, finance, and actuarial; established standards for data contracts, SLAs/SLOs, coding, and change management.
• Defined and executed the cloud data strategy; designed an Azure-enabled modern stack (Azure Data Lake, Databricks, Snowflake, dbt, ADF/GitHub Actions) using a medallion architecture to ensure audit-ready pipelines and end-to-end lineage.
• Stood up an enterprise data warehouse and shared metrics layer, accelerating actuarial analyses and self-service analytics while enforcing consistent business definitions across lines of business.
• Implemented audit-proof financial reconciliation from ingestion to the general ledger, embedding controls, data quality checks, and traceability to meet regulatory and internal audit requirements.
• Launched an AI-enabled, homegrown BDX ingestion web application with operational metric insights, cutting onboarding cycle time and enabling business users to independently manage BDX processing.
• Partnered with Finance and Operations to prioritize ingestion roadmaps tied to reconciliation milestones, ensuring production pipelines were compliant, testable, and observable from day one.
• Scaled a global data engineering team; instituted hiring bar, onboarding playbooks, and peer review practices that increased delivery velocity and reduced rework.
• Established robust DataOps practices—automated CI/CD, dbt testing, data quality SLAs, incident runbooks, and cost/reliability guardrails—improving deploy frequency and reducing time-to-restore.
• Selected and integrated the right tool mix (e.g., Databricks for cleansing, Snowflake for warehousing, dbt for modeling, ADF/GitHub Actions for orchestration/CI) to accelerate pipeline delivery and reduce total cost of ownership.
• Drove organizational change by mapping people-process-technology gaps, aligning stakeholders on a future-state architecture, and sequencing delivery to show early wins while de-risking legacy dependencies.

Technologies: Azure, Databricks, DBT, Snowflake

Kinetiq captures all TV exposure with digital speed and performance — paid, earned, and owned media unified in a single platform.

Responsibilities:

Responsible for product design and engineering for Mentions, Sponsorship and Ad Catalog lines of businesses at Kinetiq.

Product Enhancements and Development:

• Collaborate with GTM functions within Kinetiq and understand customer needs and competitive landscape prior to product conceptualization for new product design
• Conduct client facing and marketing collateral once the product goes live so that GTM functions can use it for client conversations.

Engineering & Operations:

• Building scalable data platforms that can ingest and read billions of rows of data using SQL Server stack and Apache SOLR.
• API development to expose the final data layer to customers and other internal applications using micro services design pattern.
• Institutionalize application modernization roadmap for the data distribution and reporting layers using a hybrid cloud model (with apps running both on-prem and cloud)
• Develop dashboard and visualization roadmap to untap value within the data by building compelling data narratives.

Technologies: Dot Net Stack, SQL Server

7Park data uses advanced algorithms to transform hundreds of terabytes of information into contextualized data to help sophisticated investment firms with accurate benchmarking, forecasting and strategic decision making

Responsibilities:

Manage and lead three data product pillars serving Finance, Corporate and M&E Industries:

Data Engineering:.

• Design and Build scalable and robust data pipelines.
• Design, Build and maintain a unified data model to integrate and consolidate multiple disparate datasets.
• Design and build metadata management processes to enrich incoming datasets.
• Tableau and Domo Dashboard Build.
• Data Quality.
• AWS Infrastructure (Optimize Costs and comply with Information Security policies).

Application and System Development to support data products:

• Build Time Series APIs.
• Maintain and Support Data Distribution platforms systems.
• Build, Maintain and Support Customer facing front end apps.

Operations:

• Lead Production support teams.
• Responsibility of maintaining and supporting all customer facing live data products.

Hiring and building a data engineering practice:

• Foster relevant engineering skills within the team to build a data engineering practice.

Data Products and Information Strategy:

• Build engineering roadmaps to support quarterly and annual product vision.
• Responsible for Information strategy planning to ensure that internal products are aligned and processes speak the same metadata and product lexicon.
• Research and be on the lookout for emerging technologies and Industry best practices: build a business case and pitch to leadership and invest in POCs to prove out tech capabilities to future proof data products.

Vendor Management:

• Maintain and manage partnerships with external vendors including OEM and consulting firms Technology: AWS Stack including S3, EMR, RDS, Redshift, Elastic Search, Redis, Glue and Airflow for scheduling and Domo and Tableau for visualization, Apache Spark (Databricks) and Snowflake.

Engineering KPIs

• Define,build and monitor engineering KPIs to broadly cover the following areas: Dev Effort burn down, code efficiency and best practices (Ex: Efficiency, Review Coverage, Average code commits), and production incidents and application uptime