Senior Data Engineer
First seen by Alion on Sep 28, 2026.
Data Engineer L2:
Experience: 3 - 10 years in data engineering.
Location: Bangalore.
Work Mode: Bangalore - Hybrid.
Role Overview:
Join CoffeeBeans Consulting as a Data Engineer L2 and immerse yourself in a transformative role where your expertise will directly contribute to the future of AI. Located in Bangalore, this position offers a unique opportunity to work at the forefront of data engineering, shaping the way businesses leverage their data to drive innovation. With 4 - 7 years of experience, you will play a pivotal role in building and optimizing scalable data pipelines that empower analytics and AI/ML solutions. This is not just a job; it's a chance to elevate your career in a company that values engineering excellence and client impact.
Key Responsibilities:
- Design and implement enterprise-grade Databricks Lakehouse architectures using Delta Lake and Unity Catalog.
- Build scalable batch and real-time data ingestion pipelines using Lakeflow Connect, SDP, Auto Loader, Spark, and Kafka.
- Design and implement CDC architectures using Debezium, Kafka/Kafka Connect, and relational databases such as PostgreSQL, MySQL, SQL Server, and Oracle.
- Implement streaming and event-driven data pipelines using Kafka, Spark Structured Streaming, and related technologies.
- Design and manage schema evolution and data contracts using Karapace / Schema Registry.
- Implement centralized governance using Unity Catalog, including catalogs, schemas, RBAC, row/column-level security, lineage, and data access policies.
- Develop metadata-driven ingestion frameworks, data quality, reconciliation, profiling, and observability solutions.
- Design Bronze, Silver, and Gold data layers and appropriate data modeling strategies for analytical workloads.
- Establish engineering best practices covering CI/CD, testing, deployment, monitoring, logging, and operational support.
- Use Databricks Asset Bundles (DAB) and CI/CD tools such as Jenkins/GitHub Actions for automated deployment.
- Work with cloud services such as AWS S3, IAM, networking, monitoring, and security services.
- Lead technical discussions with clients, translate business requirements into technical solutions, and drive architecture decisions.
- Troubleshoot complex data engineering, CDC, streaming, performance, and production issues.
- Mentor engineers and provide technical direction across data engineering initiatives.
Must-Have Skills:
- Strong hands-on experience with Databricks and Lakehouse architecture.
- Advanced Python and SQL skills.
- Strong expertise in Apache Spark / PySpark and distributed data processing.
- Hands-on experience with Unity Catalog and Delta Lake.
- Experience with Lakeflow Connect, SDP / Spark Declarative Pipelines, and Auto Loader.
- Strong understanding of CDC architectures using Debezium and Kafka.
- Hands-on experience with Kafka / Kafka Connect.
- Experience with Karapace or Schema Registry and schema evolution.
- Strong understanding of ETL/ELT, data modeling, data warehousing, streaming, and data integration patterns.
- Experience with production-grade data pipelines and orchestration.
- Strong understanding of cloud-native data services, particularly AWS.
- Experience with CI/CD and Databricks Asset Bundles (DAB).
- Experience leading technical implementations and working directly with business/client stakeholders.
Good to Have:
- Experience with Snowflake and dbt.
- Experience with Apache Flink or other real-time processing frameworks.
- Experience implementing data governance, lineage, security, data quality, and observability.
- Experience with AWS S3, IAM, Glue, MSK/Kafka, and cloud networking.
- Experience designing metadata-driven data platforms.
- Experience with AI/ML data platforms and GenAI workloads.
- Databricks certifications, particularly Databricks Certified Data Engineer Professional.
- AWS Data Engineering/Data Analytics certifications.
Other Expectations:
- Strong ownership and problem-solving mindset.
- Ability to balance hands-on engineering with architecture and technical leadership.
- Strong client-facing and communication skills.
- Ability to mentor and guide engineering teams.
- Willingness to adapt to new technologies and client environments.
- Willingness to travel within India and internationally for short/medium-term client assignments.
Skills
Databricks, Apache Spark, Python, SQL, Delta Lake, AWS, Data Engineering, PySpark