Responsibilities Include
Design and maintain DBT models that produce trusted datasets, features, and metrics the Data Science team relies on for analysis, experimentation, ML, and reporting.
Build and operate pipelines in Databricks - PySpark jobs and Delta/Iceberg tables - that turn raw operational events into analysis-ready data.
Develop deep familiarity with operations so the datasets, schemas, and models you ship reflect how the business actually works.
Orchestrate end-to-end data workflows in Airflow (and Prefect where it fits), with SLAs the DS team can count on for daily models, dashboards, and operational decisions.
Participate in peer code reviews and raise the bar on data quality, testing, and documentation across the team's models.
Partner with data scientists to scope, design, and productionize feature pipelines and the model-supporting data behind them.
Optimize DBT and Spark workloads for cost, performance, and reliability as data volume grows.
Learning recent technologies quickly - nobody comes into this role knowing every piece of the stack. You'll lean on peers, documentation, and experimentation to grow.

What You BringBuild a solid business
5+ years of experience building, testing, and deploying data engineering systems.




Experience with at least one distributed data system, and the ability to reason about consistency, latency, throughput, and fault tolerance.
Robust SQL and proficiency with at least one of (py)Spark, DBT, or Airflow in production.
Experience with Infrastructure-as-Code systems such as Terraform, AWS CDK, or

Pulumi.
Understanding or robust interest in supply chain and the data challenges it creates.
A self-starter who takes initiative, moves fast, and ships while collaborating on big challenges.
Enthusiastic about working closely with team members to develop creative solutions to novel problems.
Excellent written and oral communication in English.
Tech: DBT, Databricks, (py)Spark, Airflow, Prefect, SQL, Iceberg/Delta; familiarity with the broader stack (Kinesis, EMR, Sigma, Pulumi) a plus.
Comfortable using modern AI coding assistants (Claude Code, Cursor, Copilot, or similar) and experienced with AI-native workflows - prompting, agentic tooling,

evaluations, retrieval - and willing to bring them into your data engineering practice.
A plus: Demonstrated, measurable success building with LLMs, evaluating model outputs, or integrating AI into data pipelines or internal tooling.

📌 Data Engineer Secunderabad
🏢 Mobile Programming
📍 Secunderabad