Vetted Data Transformation Professionals

“Data engineer focused on marketing/web analytics and external API pipelines, handling ~10M records/week. Built Azure-based ingestion and PySpark transformations with rigorous data quality checks, then served curated datasets into Synapse/Redshift for Power BI. Also designed an Airflow-orchestrated crypto REST API pipeline with monitoring, retries/exponential backoff, schema-change detection, and backfill-friendly reprocessing.”

“Analytics-focused candidate with internship and project experience at Recotap and CoUnderscore, combining SQL, Python, and BI dashboards to turn messy marketing and engagement data into decision-ready reporting. Stands out for tying analytics work to business outcomes, including ~15% CTR improvement, identifying ~40% misattributed spend, and enabling a ~$75K budget shift through better targeting.”

“Analytics professional with Northern Trust experience focused on investment portfolio reconciliation and reporting. They combine SQL, Python, and Power BI to clean and validate high-volume financial data, automate manual processes, and align operations and accounting teams on shared metrics—driving roughly 20% improvement in reconciliation accuracy.”

“Analytics professional at Optum with hands-on experience turning messy healthcare claims data from SQL, Excel, and CRM systems into validated reporting datasets and Power BI dashboards. They also built reproducible Python workflows for claims analysis and owned an end-to-end project focused on improving claims processing efficiency through metric design, segmentation, and stakeholder-driven operational improvements.”

“Analytics professional with hands-on experience unifying marketing-platform data through Fivetran and Snowflake, building reporting views, and catching source-to-report issues like timezone-driven spend discrepancies. They also owned subscription LTV/cohort analysis and engagement tracking initiatives, partnering with e-commerce, product, and senior leadership to turn behavioral and demographic data into dashboards, lead-qualification metrics, and lifecycle marketing insights.”

“Analytics professional with hands-on experience in supply chain and logistics transformation, including enterprise data preparation in SQL, Python automation, and Power BI reporting. They highlight ownership of end-to-end digitization work at Blue Dart, where they defined operational metrics, aligned cross-functional stakeholders, and delivered measurable gains in transparency, reporting efficiency, and implementation quality.”

“ML/AI engineer with hands-on experience building production systems in financial services, including a real-time underwriting analytics platform at Hartford Financial Services. Stands out for combining classic ML, low-latency API deployment, monitoring, and emerging LLM/RAG design patterns, with measurable impact including 20% better decision accuracy, sub-200ms latency, and 5M+ records processed daily.”

kartikeya tiwari

Screened

Senior Software Engineer specializing in AI systems and platform engineering

Bangalore, India6y exp

“Backend/AI engineer with experience owning production systems in fintech and product startups, including a predictive scaling platform that cut AWS spend by 40% and an ambiguous social-intelligence feature that doubled MRR from $50K to $100K. Also building AI search and document-processing workflows, with reported 99.7% extraction accuracy and hands-on use of both classical forecasting and modern LLM stacks.”

“Backend-focused engineer with experience at Charles Schwab owning financial workflow deployments end-to-end, including API/database design, SQL optimization, Python automation, and AWS-based production stabilization. Also brings applied AI quality experience through building LLM/agent validation pipelines focused on scenario testing, edge-case detection, and reducing production risk.”

“Full-stack engineer with enterprise experience at Metasystems Inc. (and Qualcomm) building high-traffic, security-sensitive systems—owned a secure transaction processing module end-to-end using Java/Spring Boot, Python/Django, and React. Strong AWS production operations (EKS/ECS/Lambda/RDS/DynamoDB) with IaC (Terraform/CloudFormation), observability, and reliability patterns; also delivered resilient ETL/integration pipelines with idempotency/retries/backfills and achieved a 50% deployment-time reduction through CI/CD and modular refactoring.”

“LLM-focused engineer who has shipped multiple production-grade AI reliability systems: an LLM output validation/monitoring service (FastAPI) with prompt versioning and failure analytics, plus a RAG feature using embeddings/vector DBs with retrieval thresholds, schema/context validation, and safe fallbacks. Strong in evaluation loops (groundedness, schema accuracy, human review) and scalable pipelines for messy document ingestion with observability and early detection of data quality issues.”

Laasya Muktevi

Screened

Intern Machine Learning Engineer specializing in forecasting, NLP, and RAG systems

San Jose, CA5y exp

“Intern who built and deployed a production LLM-powered contract analysis system for finance teams: Azure Document Intelligence for text/table extraction plus Gemini prompting to surface key terms and risks via an async API and simple UI. Emphasizes reliability in production with fallbacks, guardrails against hallucinations, and operational concerns like latency/cost/versioning, delivering summaries in under 30 seconds instead of hours.”

Sai Swetha Bodlapati

Screened

Senior Data Engineer specializing in Spark, Kafka, and Databricks Lakehouse platforms

Dallas, TX5y exp

“Data engineer at Fidelity who built and operated a real-time financial transactions lakehouse on AWS/Databricks, processing millions of records daily with Kafka streaming. Demonstrated strong reliability and data quality practices (watermarking, idempotent Delta writes, validation/reconciliation, observability) and delivered measurable improvements (~30% faster jobs and ~30% fewer data issues) while enabling trusted gold-layer analytics for downstream teams.”

“Analytics-focused candidate with experience building SQL/Python pipelines and dashboards for donor, campaign, and website performance reporting. They have worked with messy multi-source data, standardized metric definitions, and delivered automated reporting that reportedly reduced manual effort by about 80%.”

“Healthcare analytics professional with TCS/Humana experience turning messy claims and eligibility data into reliable reporting assets using SQL and Python. They combine strong data engineering and analytics execution with stakeholder management, including automating monthly claims reporting from half a day to under 5 minutes and driving a provider outreach effort that reduced claim rejection rates by about 20%.”

“Analytics professional with healthcare experience at United Health Group, focused on turning messy claims and transaction data into reliable reporting assets. They combine SQL, Python, and Power BI to automate analysis, define operational KPIs, and build dashboards that improved stakeholder visibility and helped reduce processing time by about 22%.”

“Outbound-focused candidate with experience spanning EV charging/business development and e-commerce agency outreach. They combine cold calling, digital marketing, and message testing to generate qualified conversations, including booking local business owners to speak with a CEO about EV charger installation and app-host partnerships.”

“Early-career engineer from BNP Paribas who drove a large-scale observability modernization—selecting and implementing Prometheus/Grafana for a 2000+ server estate, then productionizing it on Kubernetes via Docker/Jenkins. Known for hands-on demos, strong documentation/templates, and pragmatic troubleshooting (including custom Python metrics) that improved visibility and cut debugging time by ~60%.”

“Backend engineer who built an AI-driven "Smart Feedback Analyzer" API (Flask → FastAPI) that processes user feedback with NLP (Hugging Face + OpenAI) and returns structured insights. Demonstrates strong production-minded architecture: stateless services, Cloud Run + Docker deployment, Redis/Celery background processing, and Postgres/SQLAlchemy performance tuning (EXPLAIN ANALYZE, indexing, N+1 fixes), plus multi-tenant data isolation via JWT/API-key derived tenant IDs.”

“Accenture data engineer who greenfielded a supply-chain lakehouse platform, building an end-to-end medallion/Delta pipeline ingesting ~1.4TB/day from 17+ ERP/WMS/TMS/shipment sources. Delivered Gold datasets to Redshift/Synapse/Databricks SQL powering Power BI/Tableau with a 99.5% SLA, while cutting runtime 30% and cloud costs 16% through Spark/Delta optimizations and robust data quality controls.”