Job Title: Senior Data Engineer
Experience : 5+ years
Location : Hyderabad
About the Role
We're looking for a Data Engineer to join our team and help build the data foundation that powers analytics, experimentation, machine learning, and operational decision-making .
What You'll Do
- Build trusted data products: Design and maintain dbt models that produce reliable datasets, metrics, and features used by Data Science, Analytics, ML, and business teams.
- Develop scalable data pipelines: Build and operate Databricks and PySpark pipelines that transform raw operational events into high-quality, analysis-ready data.
- Understand the business: Develop deep familiarity with company's operations so that the schemas, datasets, and models you build accurately represent how the business works.
- Orchestrate workflows: Build and manage end-to-end data workflows using Airflow, with Prefect where appropriate, ensuring reliable SLAs for daily models, dashboards, and operational decisions.
- Partner with Data Science: Collaborate with Data Scientists to design, productionize, and maintain feature pipelines and the data infrastructure supporting ML models.
- Raise the quality bar: Participate in code reviews and improve data quality, testing, observability, documentation, and engineering standards across the team.
- Optimize for scale: Improve dbt and Spark workloads for performance, cost, reliability, and maintainability as data volumes grow.
- Learn and adapt: Quickly pick up new technologies and approaches by working with teammates, documentation, experimentation, and hands-on problem solving.
- Embrace AI-native engineering: Use modern AI coding assistants and agentic development workflows to improve productivity, engineering quality, and experimentation.
What You Bring
- 5+ years of experience building, testing, and deploying data engineering systems in production.
- Strong SQL skills and production experience with one or more of PySpark, dbt, or Airflow.
- Experience working with at least one distributed data system, with a solid understanding of concepts such as consistency, latency, throughput, scalability, and fault tolerance.
- Experience with Infrastructure as Code, using technologies such as Terraform, AWS CDK, or Pulumi.
- Strong interest in or understanding of supply chain, logistics, or operational data challenges.
- A self-starter mindset with the ability to take ownership, move quickly, and ship high-quality solutions.
- Strong collaboration skills and enthusiasm for solving complex, novel problems with teammates.
- Excellent written and verbal communication skills in English.
- Experience with modern data technologies such as:
- dbt, Databricks, PySpark / Spark, Airflow, Prefect, SQL, Delta Lake / Apache Iceberg
- Familiarity with the broader data stack — including Kinesis, EMR, Sigma, or Pulumi — is a plus.
- Comfortable using modern AI coding assistants such as Claude Code, Cursor, GitHub Copilot, or similar tools.
- Experience with AI-native engineering workflows, including prompting, agentic tooling, evaluation, retrieval, and AI-assisted development.
- A demonstrated interest in or experience with LLMs, evaluating model outputs, or integrating AI into data pipelines and internal engineering tools is a plus.
Nice to Have
- Experience designing or working with data lakehouse architectures, particularly Delta Lake or Apache Iceberg.
- Production experience with Kinesis or other streaming technologies.
- Exposure to MLOps or working closely with ML/AI teams.
- Experience collaborating with US-based engineering teams across multiple time zones.
