Role : Senior Analytics Engineer (Customer Success)
- Shift : 4 PM - 12 AM (Overlap with USA - Eastern Time)
- Experience : 5 - 6 years (startup background strongly preferred)
- Focus mix : Data Analytics 50%, Data Engineering 30%, Customer Success 20%
Company Overview :
We are a unified, financial-services CRM with an AI agent co-pilot that connects fragmented data, automates workflows, and powers outcome-driven customer journeys - end-to-end from lead to funding, especially in lending. The platform is expanding across broader financial-services use cases beyond mortgage.
Role Purpose :
Our customers (banks, NBFCs, lenders, fintechs) want trustworthy, decision-grade data and clear insights embedded in CRM workflows. Youll be the hands-on owner who models the data, builds scalable pipelines, ships crisp BI, and partners with customer teams to drive measurable business outcomes (conversion, funding velocity, retention / upsell, agent productivity).
Key Responsibilities :
Data Analytics - 50% :
Translate customer goals into analytical frameworks, certified datasets, and BI assets (Power BI / Tableau / Metabase) with semantic layers and documentation.Build cohorts, funnels, lifetime value and propensity analyses; run A / B experiments and readouts; turn findings into actions inside CRM workflows.Define source of truth metrics (lead?app?approval?funding, NCA / roll rates overlays, agent productivity) and set up robust monitoring.Data Engineering - 30% :
Design / operate lakehouse stacks on AWS (S3 + Glue Catalog + Apache Iceberg) feeding Redshift / Postgres; build ELT / ETL in PySpark / Python.Optimize models for cost and performance; implement data contracts, tests, lineage, and CI / CD for data.Build reliable ingestion from product / CRM events and financial-services systems (e.g., loan origination, servicing, core-banking, bureau, KYC).Publish curated, self-serve datasets that power BI and CRM / AI-agent actions.Customer Success - 20% :
Lead analytical onboarding : KPI design, data readiness, success plans, enablement / training.Run QBRs with quantified impact; prioritize roadmap asks by value; turn recurring insights into playbooks inside CompanyAdvise on compliant data usage and controls in regulated environments.Technology Stack :
Cloud / Data : AWS, S3, Glue Catalog, Apache Iceberg, Redshift, Postgres, PySpark, PythonAnalytics / BI : SQL (expert), Python (pandas / NumPy), Power BI / Tableau / MetabaseNice to have : dbt, Airflow / Step Functions, Terraform, GitHub Actions, event streaming (Kinesis / Kafka), reverse ETLQualifications :
5-6 years across analytics + data engineering, ideally in startup or high-ownership environments.Fluency in SQL and dimensional / data-vault modeling; you turn messy multi-source data into clean, documented, high-trust datasets.Hands-on lakehouse experience (Iceberg on Glue), plus Redshift / Postgres performance tuning.Analytical storytelling : you connect metric movements to operational levers and ship changes that move the funnel.Domain comfort with financial-services data (PII handling, consent, encryption, data residency; familiarity with SOC 2 / GDPR / PCI principles).Customer-facing strength : translate technical detail into business impact; handle exec and ops stakeholders with ease.Good to have :
Experience instrumenting product / CRM events and mapping to lending life-cycle stages.Exposure to AI / agent-driven workflows (prompted actions, guardrails, evaluation).Building cost-aware data stacks and usage-based BI governance at scale.(ref : hirist.tech)
