Senior Data Engineer

CoffeeBeans; Bengaluru; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

CoffeeBeans seeks seasoned data engineers to own engagements from architecture through delivery in Bengaluru. You will lead data initiatives, guide teams, and build scalable data platforms supporting BI, analytics, and AI/ML workloads.

You will design high‑performance pipelines, model data lakes and warehouses, and set data standards across stakeholders. Mentoring junior engineers and ensuring CI/CD best practices are integral to the role.

Qualifications

• Proficient in Python and SQL with strong data handling capabilities.
• Experience with Databricks and distributed processing frameworks.
• Understanding of ETL/ELT, data modeling (dimensional and normalised).
• Knowledge of data lakes, lakehouses, and warehousing for analytics.

Responsibilities

• Lead end‑to‑end data engineering engagements from architecture to delivery.
• Design scalable, secure data architectures and pipelines (batch/real‑time).
• Model and implement data lakes, lakehouses, and warehouses for analytics.
• Collaborate with stakeholders to define data strategies and standards.
• Mentor engineers and guide technical decision‑making.
• Ensure code quality, observability, testing, and CI/CD in pipelines.
• Troubleshoot complex data issues across heterogeneous systems.

Skills

Python

SQL

Data modeling

ETL/ELT

Tools

Databricks

Apache Spark

Flink

Job description

We’re looking for seasoned data engineers who can take end-to-end ownership of complex data engineering engagements—right from architecture and design to development and delivery. You’ll work with client stakeholders, lead data initiatives, and guide teams in building modern, scalable data platforms that support business intelligence, advanced analytics, and AI/ML applications.

Responsibilities

• Lead the design and implementation of scalable, secure, and high-performance data architectures.
• Architect and optimise data pipelines for batch and real-time processing across varied source systems.
• Model and implement enterprise-grade data lakes, lakehouses, and warehouses to support analytical workloads.
• Collaborate with client teams and internal stakeholders to define data strategies, tooling, and standards.
• Set up and enforce best practices around code quality, pipeline observability, testing, and CI/CD.
• Guide technical decision-making and mentor junior/mid-level engineers.
• Troubleshoot complex data issues and provide architectural solutions across heterogeneous systems.

Qualifications

• Strong expertise in Python and advanced proficiency in SQL.
• Hands-on experience with Databricks.
• Deep understanding of data engineering architecture: ETL/ELT pipelines, data modeling (dimensional and normalised), warehousing, streaming systems, and data integration patterns.
• Proven experience with distributed data processing frameworks (e.g., Apache Spark, Flink).