Vetted Data Quality Professionals

“Growth and loyalty leader who built TangerClub into a large-scale PLG-style membership ecosystem, owning strategy, data infrastructure, lifecycle marketing, and optimization across 10M+ customer profiles. Particularly compelling for growth roles because they translated SaaS-style activation, retention, and experimentation principles into a complex omnichannel retail environment, driving 618K new profiles and about $11M in attributable email revenue in 2025.”

“Senior engineering leader with 8+ years of hands-on and people leadership experience across data-intensive enterprise platforms. He has led legacy-to-AWS modernization for mission-critical identity data workflows at Deep Sync, built and scaled teams rapidly, and previously helped create a 0-to-1 enterprise analytics platform at Kantar that later scaled to handle 10x more data with major performance gains.”

Kamilah Patterson

Screened ReferencesModerate rec.

Senior HR Business Partner specializing in talent strategy and workforce analytics

Brooklyn, New York11y exp

“HR Business Partner with GroupM experience supporting multiple mergers, building and transitioning Analytics/Programmatic/Account Management teams through hands-on job architecture, benchmarking, and performance management. Uses HR analytics (including an Excel-based retention model with visual reporting) and cross-functional partnership with IT to reduce disruption and improve retention during restructuring and major policy changes like return-to-office.”

“Backend/data engineer who has maintained high-traffic FastAPI microservices and delivered a hybrid AWS serverless+containers platform using Terraform and GitHub Actions, with secrets managed via Secrets Manager/SSM. Also led modernization of a mission-critical 10,000+ line SAS financial reporting engine into Python microservices and built AWS Glue ETL pipelines feeding a centralized data lake.”

“AI/ML engineer with CVS Health experience deploying production LLM systems in regulated healthcare settings, including a large-scale RAG solution (1M+ documents) built for compliance-grade, auditable policy/regulatory Q&A with strong anti-hallucination controls. Also delivered an NLP summarization system for physician notes/case narratives by partnering closely with non-technical care operations stakeholders and iterating via prototypes, dashboards, and feedback loops.”

“Data/ML practitioner with experience at Airtel and Lincoln Financial delivering measurable business outcomes: improved retention 15% via NLP sentiment analysis and cut response time ~25% using sentence-BERT + FAISS semantic linking. Strong in data quality/identity resolution (SQL + fuzzy matching) and in building production-grade Python workflows orchestrated with Airflow/AWS Glue, including validation and dashboard integration in Power BI.”

“Backend engineer with American Express experience maintaining an internal Python/Flask rewards simulation microservice used by product analysts and QA. Demonstrated strong performance and scalability work: moved batch simulations to Celery, added Redis caching to cut DynamoDB latency, and tuned Postgres/SQLAlchemy queries with EXPLAIN ANALYZE and composite indexes (bringing API responses under ~200ms by queueing jobs). Also has experience integrating ML via Flask-based model-serving APIs (scikit-learn/LightGBM packaged with joblib) and designing multi-tenant data isolation and tenant-specific configuration systems.”

“Data engineer focused on AWS-based enterprise data platforms, owning end-to-end pipelines from multi-source batch/stream ingestion (Glue/Kinesis/StreamSets/Airflow) through PySpark transformations into curated datasets for Redshift/Snowflake. Emphasizes production reliability with strong monitoring/observability and data quality gates, and reports ~30% performance improvement plus improved SLAs and latency after optimization.”

“Data/analytics engineer who owned Angi’s service-request funnel event pipeline end-to-end, routing events server-side to bypass ad blockers and recovering ~15% lost tracking at millions of events/day. Built Snowflake/dbt reporting tables powering Looker dashboards, with strong emphasis on validation, monitoring/alerting, and safe schema evolution. Also shipped a reusable flow state management backend service with TTL storage, CI/CD, and developer-friendly APIs.”

“Data engineer focused on reliable, production-grade pipelines and data services: has owned end-to-end ingestion-to-serving workflows processing millions of records/day, using Airflow, Python/SQL, and PySpark. Demonstrates strong operational rigor (monitoring, retries, idempotency, backfills) and measurable outcomes (98% stability, ~30% faster processing), plus experience exposing curated warehouse data via versioned REST APIs.”

“Data engineer in a banking context who has owned end-to-end Azure lakehouse pipelines ingesting financial/vendor data from APIs, Azure SQL, and flat files into Databricks/Delta (bronze-silver-gold). Emphasizes production reliability via schema-drift validation, data quality controls, monitoring/alerting, retries/checkpointing, and Spark/Delta performance tuning, with outputs served to BI/reporting teams (e.g., Tableau).”