Data & Analytics Engineer - Risk Analytics

DEEKSHA DEEPAK INGLE (available from September ***6)

Boston, MA 857-***-**** *****.**@************.*** linkedin.com/in/deekshaingle SUMMARY

Results-driven Data & Analytics professional with 3+ years of experience designing scalable data pipelines, enterprise warehouse solutions, and analytics systems across Snowflake, Teradata, Oracle, and AWS. Combines deep technical expertise in ETL/ELT, Python, and SQL with strong business acumen developed through graduate-level coursework in Enterprise Risk Management. Experienced in building data-driven frameworks, risk dashboards, and executive-ready reporting seeking roles at the intersection of data engineering, risk analytics, and business intelligence. SKILLS

Data Warehouse & OLAP: EDW design, OLAP platforms (Snowflake, Teradata, Oracle, Redshift), columnar storage, star/snowflake schema, SCD Type 1/2, data mart design, metadata modeling

ETL/ELT & Big Data: ETL/ELT pipeline design, data modeling, Apache Spark Hadoop, Apache Airflow, Kafka, Informatica, dbt, data lake architecture, large-scale data integration

Languages: SQL, DDL, Python, Bash, R

Cloud, Non-Relational & BI: AWS (S3, Glue, EMR, Redshift, Lambda), MongoDB (document store), key-value stores, Docker, Kubernetes, CI/CD, Tableau, Power BI

PROFESSIONAL EXPERIENCE

Student Ambassador Northeastern University, Boston March 2026-Present

• Conduct 175–200 outbound calls per week to prospective students, guiding them through the application process, resolving enrollment queries, and connecting high-intent candidates with enrollment counselors serving as the human face of Northeastern's admissions experience.

• Delivered personalized outreach via calls and voicemails across 20 hours/week, communicating program value and student opportunities at Northeastern while maintaining conversation quality and follow-through to support enrollment conversion goals. Software Engineering Intern Greenstand, Anchorage, AK April 2026- July2026

• Built an event-driven ETL data integration pipeline (REST API Pub/Sub Firestore) consolidating data from disparate mobile, GPS, and image metadata sources into a structured data store enabling real-time availability for downstream BI reporting and operational decision-making.

• Designed schema validation and idempotent write logic for distributed data ingestion; authored data flow diagrams and pipeline architecture specifications adopted as the onboarding standard for new contributors. DWH/BI Developer Amdocs, Pune, India Nov 2021 – Aug 2024

• Designed and operated large-scale EDW solutions across Snowflake, Teradata, and Oracle migrating ~10 TB of enterprise data including structured and semi-structured (JSON) datasets into unified warehouse layers.

• Built reusable ELT ingestion frameworks (Informatica + dbt) delivering star schema and SCD Type 2 transformation layers across ~800 production tables; maintained data quality SLAs with defect escape rates below 0.5%.

• Optimized distributed ETL at scale; tuned SQL and Oracle query performance via EXPLAIN plans and Linux profiling tools, delivering measurable speedups on OLAP queries powering executive BI dashboards.

• Partnered with business owners to translate key business questions into scalable data models; delivered Tableau and Power BI self-service dashboards replacing manual Excel workflows for three operational teams.

• Orchestrated nightly batch pipelines via Apache Airflow DAGs and shell script automation, eliminating ~900 hours of manual data preparation annually; standardized ETL validation service deployment across dev and production.

• Supported live data pipeline operations and on-call incident response for a media monitoring platform processing high-volume real-time data stream with strict uptime SLAs.

Quality Test Engineer Critical Mention (Onclusive), Mumbai, India July 2021-oct 2021

• Owned end-to-end QA lifecycle for a media monitoring platform triaged Jira story, executed functional, smoke, and regression testing across QA and production environments, and managed Jenkins deployments to push validated fixes to prod with full ticket documentation.

• Maintained 98%+ ticket accuracy across all QA cycles, reducing bug escape rate to near-zero in production and cutting average defect resolution time by 35% enabling the engineering team to ship faster with higher release confidence. PROJECTS

Enterprise Risk Assessment – elf. Beauty Acquisition of Rhode ($1B M&A) April 2026– May2026

• Conducted end-to-end enterprise risk assessment of a $1B acquisition using a COSO-aligned framework, identifying six risk categories: strategic, reputational, financial, operational, regulatory, and integration

• Built a quantitative Risk Register with likelihood/impact scoring, EMV calculations, and color-coded Risk Score matrices across four prioritized risk categories

• Developed a Key Risk Indicator (KRI) Brand Sentiment Score to monitor key-person dependency risk, with measurable trigger thresholds tied to deal earnout structure

• Applied qualitative and quantitative risk analysis including probability-impact matrices and heat maps to support executive-level risk decision- making

Customer Churn & Revenue Impact Analysis Feb 2026 –March 2026

• Built an end-to-end customer churn analysis dashboard in Tableau for a telecom dataset, structured around five core business questions spanning churn severity, pricing behavior, and revenue impact

• Created calculated fields for churn rate, revenue lost, and high-risk customer segmentation; applied filters and aggregations to ensure statistical accuracy across chart types

• Identified month-to-month contracts as the highest churn driver and quantified revenue loss by contract type, translating churn patterns into measurable business impact

• Isolated a high-risk customer segment (month-to-month + high monthly charges) to enable targeted retention strategy recommendations Finance RAG System on SEC Filings Feb 2026 –March 2026

• Built a Retrieval-Augmented Generation (RAG) pipeline on SEC 10-Q filings to enable accurate, evidence-backed financial Q&A prioritizing auditability over generative hallucination

• Ingested and processed SEC filings (facts & submissions), converting financial metrics into document chunks; generated embeddings and indexed them using FAISS for semantic vector search

• Implemented top K retrieval to surface the most relevant financial evidence per query supporting questions on debt repayments, financing activities, and quarterly metrics

• Designed for high-trust, high-stakes financial domains: system answers only when underlying data supports the response, exposing traceable evidence for manual or programmatic synthesis

EDUCATION

• Master of Professional Studies in Analytics, AI/ML Concentration Northeastern University, Boston, 2026

• Coursework: Data Mining, Machine Learning with AI, Generative AI, Applications of Artificial Intelligence, Risk Managemental Analytics

Contact this candidate