Snowflake Data Architect / Lead Data Engineer

Experience: 710 Years | Role: Individual Contributor + Project Lead

What You'll Do

Lead end-to-end design, build, and delivery of enterprise data platforms — lakehouse architectures, cloud data warehouses, and analytics solutions for regulated industries (banking/financial services preferred). You'll own the solution architecture, guide a team of engineers, and be the technical authority on client engagements.

Key Responsibilities

- Architect and deliver enterprise lakehouse and data warehouse solutions on AWS (S3, Glue, EMR, Redshift) with Snowflake as the core analytical engine.
- Design multi-zone data lake architectures (Bronze / Silver / Gold) using open table formats (Delta Lake, Apache Iceberg) with ACID compliance and schema evolution.
- Build and optimise data pipelines — batch, CDC, and real-time streaming — using tools like AWS DMS, Glue Spark, MSK (Kafka), dbt, and Step Functions/Airflow.
- Define data models: Data Vault 2.0 for integration layers, dimensional star/snowflake schemas for consumption, and semantic layers for enterprise BI.
- Implement data governance frameworks — cataloguing, lineage, data quality (Great Expectations or equivalent), MDM, and BCBS 239 / regulatory compliance controls.
- Lead solution design workshops, produce architecture documents, and present to C-level and steering committees.
- Mentor and technically guide a team of 4–6 data engineers across delivery phases.
- Manage stakeholder expectations, delivery timelines, and quality gates on engagements sized at 6–12+ months.

Must-Have

- 7–10 years in data engineering / data architecture roles,



with at least 4 years on Snowflake (Enterprise or Business Critical edition).
- Robust hands-on experience with AWS data services — S3, Glue, EMR, Redshift, DMS, Lambda, Step Functions, IAM, KMS.
- Proven delivery of at least 2 large-scale data platform implementations (50+ users, 10+ source integrations, TB-scale).
- Deep expertise in SQL, Python/PySpark, and data modelling (Data Vault, star schema, bi-temporal).
- Experience with ETL/ELT orchestration tools — dbt, Airflow (MWAA), or equivalent.
- Working knowledge of data governance, data quality frameworks, and metadata management.
- Ability to produce solution architecture documents, cost estimates, and technical proposals.
- Excellent communication — comfortable leading client-facing discussions and presenting to senior leadership.

Good to Have

- Experience in banking, financial services, or other regulated industries (RBI/RMA compliance, PCI DSS, ISO 27001).
- Exposure to AI/ML platforms — SageMaker, MLOps, Feature Stores, or GenAI (Bedrock/RAG).
- Snowflake certifications (SnowPro Advanced: Architect or Data Engineer).
- AWS Solutions Architect or Data Analytics Specialty certification.
- Experience with Oracle Analytics Cloud, Power BI, or Tableau for enterprise BI delivery.
- Familiarity with DevSecOps, CI/CD for data pipelines, and infrastructure-as-code (Terraform/CloudFormation).
- Prior experience with historical data migration and source-to-target reconciliation at scale.

Why This Role

Work on high-impact, greenfield enterprise data platforms for banking and financial services clients. Own the architecture, shape the solution, and lead delivery end-to-end.

📌 Snowflake Data Architect / Lead Data Engineer (Pune)
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