Job Title : Lead Data Architect ML & BI

Experience : 10 - 15 years

Role Overview :

Zinnobyte's Client is looking for a hands-on Lead Data Architect to design and build the next-generation data, machine learning, and business intelligence platform for our HR, Payroll, and Benefits SaaS ecosystem. This is a builders role perfect for someone who thrives on writing code, deploying models, architecting pipelines, and enabling intelligent reporting systems for real-world users.

You will work closely with product and engineering teams to drive a data-first culture by delivering clean, well-modeled, governed, and actionable data to power automation, analytics, and decision-making across the platform.

Key Responsibilities :

1) Data Engineering & Architecture :

- Design and implement scalable ETL/ELT pipelines for batch and real-time data processing.

- Architect data lakes, marts, and warehouses using best-in-class modeling principles (e.g., star schema, dimensional modeling).

- Maintain and optimize multi-tenant data access and ensure system security, masking, and role-based access controls (RBAC).

2) BI and Reporting Systems :

- Own the architecture and delivery of reporting frameworks that serve both internal users and external customers.

- Enable scheduled, ad hoc, and role-based reporting via embedded dashboards or APIs.

- Define core business metrics, governance rules, and source-of-truth data structures.

3) ML Model Development :

- Build, deploy, and monitor machine learning models for use cases like anomaly detection, predictions, and intelligent assistants.

- Collaborate with product teams to translate real-world problems into deployable ML solutions (e.g., using scikit-learn, XGBoost, or LLMs).

- Implement MLOps practices for model training pipelines, version control, and performance monitoring.

4) Data Governance & Compliance :

- Define and implement data quality checks, profiling, validation, and lineage tracking.

- Ensure compliance with PII/PHI regulations (HIPAA, ISO 27701), working closely with security and infrastructure teams.

- Guide tagging, cataloging, and metadata strategies to support data discoverability and trust.

Required Skills & Qualifications :

- Education : B.Tech/M.Tech in Computer Science, Data Science, or related field.

- Experience : 10+ years in data engineering or architecture, including 3+ years delivering ML or BI solutions.

- Expert in Python, SQL, and data engineering frameworks (e.g., Airflow, dbt, custom pipelines).

- Deep knowledge of PostgreSQL, data warehousing, and multi-tenant SaaS data design.

- Hands-on experience building and deploying machine learning models in production environments.

- Experience with BI/reporting tools (Metabase, Superset, Looker, or embedded dashboards).

- Strong understanding of data security, governance, and compliance best practices.

- Familiarity with AWS data stack (e.g., S3, RDS, Glue, Redshift, Lambda) is preferred.

Nice to Have :

- Exposure to LLM/AI frameworks (e.g., LangChain, RAG pipelines, vector databases).

- Experience with event-driven architecture, messaging systems (Kafka, RabbitMQ).

- Prior experience with multi-tenant data platforms in SaaS or HR/payroll domain.

- Familiarity with observability tools (e.g., Prometheus, Grafana, OpenTelemetry).