Senior Data Engineer (Contract) [HR208]

Lever, Inc.; Hyderabad; Remote; INR 11,561,000 - 17,341,000; Full time

Benefits offered by this job

Remote-first culture

Job summary

Smart Working is seeking a Senior Data Engineer to design and scale data infrastructure powering an AI assistant for property managers and build-to-rent teams. You will architect real-time data pipelines, vector databases, and ML data workflows, collaborating with AI, backend engineering, product teams, and leadership.

As the inaugural senior data hire, you will define data architecture, tooling, standards and the hiring bar, setting foundations for a scalable data function in a remote-first,

Qualifications

• 7+ years in dedicated data engineering roles.
• Experience building scalable data pipelines and distributed systems.
• Experience with PostgreSQL and NoSQL databases.
• Familiarity with vector databases for AI workloads.
• Strong Python programming skills and data modeling expertise.

Responsibilities

• Architect and build scalable data pipelines and infrastructure for AI and product systems.
• Design data ingestion, transformation and storage for operational and AI workloads.
• Develop and manage batch and real-time data pipelines.
• Build and optimise systems for vector search and ML data workflows.
• Ensure data reliability, security and governance across the platform.
• Collaborate with AI and backend teams to support training and inference.
• Implement monitoring, observability and data quality frameworks.
• Contribute to technical architecture decisions and long-term data strategy.
• Define culture, standards and hiring bar for the data function as it scales.
• Partner with founders to translate data capabilities into product decisions.

Skills

Python programming

Data pipelines

Distributed systems

Relational databases

PostgreSQL

NoSQL

Vector databases

Spark

Airflow

Kafka

Elasticsearch/OpenSearch

Machine learning data workflows

Data modeling

Polars/Pandas

Tools

PostgreSQL

MongoDB

Qdrant

Milvus

pgvector

Apache Spark

Apache Airflow

Kafka

Elasticsearch/OpenSearch

Python data libs (Pandas, Polars)

Job description

About Smart Working

At Smart Working, we believe your job should not only look right on paper but also feel right every day. This isn’t just another remote opportunity - it’s about finding where you truly belong, no matter where you are. From day one, you’re welcomed into a genuine community that values your growth and well-being.Our mission is simple: to break down geographic barriers and connect skilled professionals with outstanding global teams and products for full-time, long-term roles. We help you discover meaningful work with teams that invest in your success, where you’re empowered to grow personally and professionally.

Join one of the highest-rated workplaces on Glassdoor and experience what it means to thrive in a truly remote-first world.

About the Role

As a Senior Data Engineer, you will build and lead the data infrastructure powering an intelligent AI assistant that automates operational workloads for property managers, letting agents and build-to-rent teams. The platform manages communications, compliance, maintenance coordination and scheduling, helping property businesses operate with the efficiency of modern digital platforms.

You will architect and scale the systems behind the AI products, spanning real-time data pipelines, analytics infrastructure, vector databases and machine learning data workflows. Working closely with AI engineers, backend engineers, product teams, founders and product leadership, you will ensure the platform can process large volumes of operational data reliably and intelligently.

As the first senior data hire, you will define the data architecture, tooling, engineering standards, culture and hiring bar, building the foundations of the future data team.

Responsibilities

• Architect and build scalable data pipelines and infrastructure to support AI and product systems.
• Design and maintain data ingestion, transformation and storage architectures for operational and AI workloads.
• Develop and manage batch and real-time data pipelines.
• Build and optimise systems for vector search, retrieval and machine learning data pipelines.
• Ensure data reliability, security and governance across the platform.
• Collaborate with AI and backend engineering teams to support training, inference and product features.
• Implement monitoring, observability and data quality frameworks.
• Optimise the performance of large-scale datasets and query systems.
• Contribute to technical architecture decisions and long-term data strategy.
• Act as the founding data hire, defining culture, standards and the hiring bar for the data function as it scales.
• Partner directly with founders and product leadership to translate data capabilities into product decisions.

Requirements

• 7+ years of professional experience, with the majority of that experience in dedicated data engineering roles.
• Strong experience designing and building data pipelines and distributed data systems.
• Experience working with relational databases, with PostgreSQL preferred, although MySQL or similar is acceptable.
• Experience working with NoSQL databases.
• Experience with vector databases used in modern AI systems.
• Strong programming experience in Python.
• Demonstrated ability to make and justify architectural decisions, rather than only implementing them.
• Experience building scalable backend systems.
• Experience designing data models and storage architectures.
• Strong understanding of data processing performance and optimisation.
• Experience with some of the following data frameworks and infrastructure technologies is highly desirable: Apache Spark, Apache Airflow, Kafka, and Elasticsearch or OpenSearch.
• Experience with relevant database technologies is highly desirable, including PostgreSQL, MongoDB, and vector databases such as Qdrant, Milvus or pgvector.
• Experience with Python data-processing libraries such as Pandas or Polars is highly desirable.

Nice to Have

• Experience working on AI or machine learning platforms.
• Familiarity with stream processing and event-driven architectures.
• Experience with cloud infrastructure such as GCP, AWS or Azure.
• Experience working in high-growth startups or early-stage companies.