Senior Data Engineer

Role Overview

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.

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.

Required 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 platformAWS, GCP, or Azureand 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.