About Us :

Hero FinCorp Group is building HomeVerse, India's first fully digital home ownership ecosystem.

We are building a high-quality technology organization with strong engineering foundations, modern architecture, and a product mindset focused on speed, reliability, and scale.

About the Role :

We are seeking a Senior Data Engineer to architect and build a scalable, production-grade data platform that supports product analytics, operational intelligence, AI/ML use cases, and decision-making across HomeVerse.

This is a senior individual-contributor role with significant ownership over architecture, engineering standards, reliability, and the evolution of the data ecosystem.

Total Experience : 8 - 12 years

Key Responsibilities :

- Design and build scalable batch and real-time data pipelines across product databases, third-party APIs, event streams, and unstructured sources.

- Own data architecture across ingestion, storage, transformation, orchestration, serving, observability, and access layers.

- Develop robust ELT/ETL workflows with strong handling of schema evolution, backfills, retries, lineage, and data freshness.

- Establish data quality, testing, reconciliation, monitoring, and alerting standards for business-critical datasets.

- Build performant data models and semantic layers that enable reliable self-service analytics and downstream consumption.

- Partner with backend, product, analytics, and AI/ML teams to define data contracts and production-grade interfaces.

- Make sound build-versus-buy and architectural decisions, balancing scalability, maintainability, security, and cost.

- Implement governance, access control, privacy, and auditability appropriate for a financial-services environment.

- Drive infrastructure-as-code, CI/CD, code review, documentation, and engineering standards across the data stack.

- Mentor engineers and contribute to technical hiring and capability building as the function scales.

Qualifications :

- Minimum 8 years of relevant experience in data engineering, including ownership of large-scale or business-critical data platforms.

- Expert-level SQL and strong Python; solid software engineering fundamentals including testing, version control, modular design, and code review.

- Deep hands-on experience with at least one major cloud platform - AWS, GCP, or Azure - and modern cloud data architecture.

- Strong experience with orchestration frameworks such as Airflow, Dagster, or Prefect and transformation frameworks such as dbt or SQLMesh.

- Hands-on experience with modern warehouses or lakehouses such as Snowflake, BigQuery, Databricks, Delta Lake, or Apache Iceberg.

- Experience with streaming, event-driven, or CDC architectures using technologies such as Kafka, Kinesis, Redpanda, Debezium, or equivalents.

- Strong understanding of data modeling, query optimization, distributed systems, partitioning, storage formats, and performance tuning.

- Experience implementing data quality and observability using frameworks such as Great Expectations, Soda, Monte Carlo, or equivalent approaches.

- Experience with infrastructure-as-code and deployment automation using Terraform, Pulumi, or equivalent tooling.

- Demonstrated ability to independently evaluate technical trade-offs and defend architectural decisions.

Preferred Qualifications :

- Experience supporting AI/ML and LLM workloads, including embeddings, vector stores, RAG pipelines, feature computation, and evaluation datasets.

- Experience in fintech, lending, payments, banking, insurance, or another regulated environment.

- Experience with data catalogs, lineage, governance, and metadata platforms such as DataHub, OpenMetadata, or Unity Catalog.

- Experience optimizing cloud and data infrastructure costs at scale with measurable impact.

- Prior experience as a senior or founding data engineer in a high-growth product organization.

What This Role Offers :

- The opportunity to shape the data architecture and engineering standards of a rapidly growing digital platform, with substantial autonomy over foundational technical decisions and direct exposure to product, business, and technology leadership.