Data Engineer

Xenonstack; Mohali, Chandigarh; On-site; INR 1,200,000 - 2,100,000; Full time

Job summary

Xenonstack is seeking a data engineer in Mohali to build and operate data pipelines powering AI workloads. The role focuses on batch and streaming workflows, ETL/ELT orchestration, and lakehouse ingestion using Python, SQL, Airflow, and Spark.

You will model data for analytics and ML, ensure end-to-end data quality, and optimize costs and performance while implementing governance. 2–4 years of experience required.

Qualifications

• 2–4 years of professional data engineering experience.
• Strong Python and advanced SQL with window functions and joins.
• Hands-on with production data pipelines and orchestration tools.
• Experience with distributed processing (Spark/PySpark) in cloud platforms.
• Knowledge of data modelling and one cloud data platform (Databricks/Snowflake/BigQuery/Redshift).
• Git, Docker and at least one cloud provider (AWS/Azure/GCP).
• Familiarity with AI coding assistants and reviewing generated code.

Responsibilities

• Build and operate batch and streaming data pipelines.
• Design, develop, and maintain ETL/ELT workflows with orchestration tools.
• Work with Spark on large datasets and manage lakehouse ingestion.
• Model data for analytics and ML consumption, including feature prep.
• Own data quality, tests, lineage, and monitoring end-to-end.
• Optimize for cost and performance; manage access controls and governance.
• Collaborate directly with AI engineering, platform, and product teams.

Skills

Python

Advanced SQL

Window functions

Query optimisation

Complex joins

Airflow

Dagster

Prefect

PySpark

Databricks

Snowflake

BigQuery

Redshift

Git

Docker

Cloud providers (AWS/Azure/GCP)

Data modelling

AI coding assistants

Education

BE / B.Tech in Computer Science or related field

Tools

Airflow

Dagster

Prefect

Spark (PySpark)

Databricks

Snowflake

BigQuery

Redshift

Kafka

Flink

Delta Lake / Iceberg / Hudi

dbt

Kubernetes

Terraform

CI/CD

Job description

Role & responsibilities

Build and operate the data pipelines that agentic AI systems run on.

• Design, build, and maintain batch and streaming data pipelines in Python and SQL
• Build and orchestrate ETL/ELT workflows using Airflow, Dagster, or similar
• Work with distributed processing frameworks Spark or equivalent — on large datasets
• Ingest data from APIs, databases, files, and event streams (Kafka or similar) into the lakehouse
• Model data for analytics and for machine learning consumption, including feature preparation
• Build and maintain the data layer behind retrieval-augmented generation — chunking, embedding pipelines, and vector store population
• Own data quality end to end: validation, testing, monitoring, lineage, and alerting on pipeline failures
• Optimise for cost and performance — query tuning, partitioning, storage format decisions
• Implement access controls and governance appropriate to enterprise and regulated customers
• Work directly with AI engineering, platform, and product rather than through handoffs

You will be building the data foundation for Agentic AI solutions and system, where pipelines feed live agent workflows, not just dashboards, and both correctness and latency matter.

Preferred candidate profile

• 2–4 years of professional data engineering experience
• Strong Python and advanced SQL — window functions, query optimisation, complex joins
• Hands-on experience building production pipelines with an orchestration tool (Airflow, Dagster, Prefect)
• Experience with a distributed processing framework, typically Spark (PySpark)
• Working knowledge of a cloud data platform — Databricks, Snowflake, BigQuery, or Redshift
• Comfortable with Git, Docker, and at least one cloud provider (AWS, Azure, or GCP)
• Understanding of data modelling — dimensional modelling or one big table, and when each applies
• Fluent with AI coding assistants in your daily workflow, and able to review what they produce rather than ship it unread
• B.E. / B.Tech in Computer Science, Data Engineering, or a related field. Strong self-taught engineers with demonstrable production work are equally welcome

Good to have

• Streaming experience — Kafka, Flink, Spark Structured Streaming
• Lakehouse table formats — Delta Lake, Apache Iceberg, or Hudi
• dbt for transformation and testing
• Vector databases and embedding pipelines for RAG
• Kubernetes, Terraform, or CI/CD for data infrastructure
• Data observability and lineage tooling
• Exposure to feature stores or ML pipeline tooling