Senior Data Engineer

About Calfus At Calfus, we are known for delivering cutting-edge AI agents and products that transform businesses in ways previously unimaginable. We empower companies to harness the full potential of AI, unlocking opportunities they never imagined possible before the AI era. Our software engineering teams are highly valued by customers, whether start-ups or established enterprises, because we consistently deliver solutions that drive revenue growth. Our ERP solution teams have successfully implemented cloud solutions and developed tools that seamlessly integrate with ERP systems, reducing manual work so teams can focus on high-impact tasks. None of this would be possible without talent like you! Our global teams thrive on collaboration, and we’re actively looking for skilled professionals to strengthen our in-house expertise and help us deliver exceptional AI, software engineering, and solutions using enterprise applications. As one of the fastest-growing companies in our industry, we take pride in fostering a culture of innovation where new ideas are always welcomed—without hesitation. We are driven and expect the same dedication from our team members. Our speed, agility, and dedication set us apart, and we perform best when surrounded by high-energy, driven individuals. To continue our rapid growth and deliver an even greater impact, we invite you to apply for our open positions and become part of our journey!

About the Role As a Senior Data Engineer – BI Analytics & DWH, you will play a pivotal role in building and scaling enterprise-grade data infrastructure and intelligent data pipelines that power our business intelligence and analytics capabilities. With 6–9 years of experience, you will bring deep expertise in ETL/ELT development, advanced data modelling (dimensional and DWH), and hands-on proficiency in modern platforms such as Databricks or Snowflake. A strong foundation in data visualization — preferably Power BI — is essential, as you will be expected to bridge the gap between data engineering and business insight delivery.

What You'll Do ETL/ELT Development:

• Design, build, and maintain efficient ETL and ELT processes using tools such as Azure Data Factory, Databricks, Snowflake, or similar orchestration frameworks.
• Ensure reliable, high-performance data ingestion from diverse sources into centralized storage systems (DWH/data lakes) with robust error handling and monitoring.
• Implement incremental load strategies, CDC patterns, and watermarking logic to optimize pipeline efficiency at scale.

Data Modelling & Warehousing (Core Requirement):

• Design and own relational and dimensional data schemas — including Star Schema, Snowflake Schema, and Data Vault — tailored to business use cases in data lakes or traditional data warehouses.
• Independently architect fact and dimension table structures, define SCD strategies, and ensure models are optimized for analytics and reporting workloads.
• Demonstrate deep, hands-on data modeling ownership — not just implementation within frameworks defined by others.
• Develop and maintain data models in platforms such as Databricks (Delta Lake) or Snowflake, ensuring scalability, performance, and data integrity.

Databricks / Snowflake Expertise (Core Requirement):

• Demonstrate in-depth, project-level hands-on experience in Databricks (Delta Lake, Unity Catalog, Databricks Workflows, cluster optimization, Z-ORDER, AQE) OR Snowflake (multi-cluster warehouses, clustering keys, COPY INTO, performance tuning, RBAC). Both preferred.
• Go beyond notebook-level execution — ownership of architecture, platform configuration, and performance engineering is expected.
• Leverage PySpark and Spark SQL for large-scale distributed data processing, transformation, and optimization.

Database Engineering:

• Write efficient, optimized SQL queries, stored procedures, and transformation logic to clean, aggregate, and serve analytical workloads.
• Manage complex data transformations, performance tuning, and query plan analysis across large datasets.

BI & Visualization (Required — Power BI Preferred):

• Hands-on experience with Power BI (preferred) or Tableau — including dashboard development, DAX measures, star schema data model design for BI consumption, and RLS implementation.
• Own the BI layer end-to-end: from data model design to dashboard delivery, not just data pipeline support.
• Collaborate with analysts and business stakeholders to translate reporting requirements into scalable, self-service BI solutions.
• Candidates with zero visualization experience will not be considered regardless of data engineering depth.

Performance & Data Quality:

• Monitor, troubleshoot, and optimize data pipelines and warehouse jobs to ensure timely, accurate, and reliable data delivery.
• Implement data quality frameworks — validation checks, reconciliation logic, anomaly detection — to ensure trustworthiness of data outputs.
• Own production support with SLA adherence and proactive incident resolution.

What We're Looking For Must-Have:

• Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related field.
• 6–9 years of hands-on experience in data engineering with proven ETL/ELT development and advanced data modelling.
• Deep, independently owned expertise in Databricks (Delta Lake, Unity Catalog, cluster optimization) OR Snowflake (DWH design, performance tuning, RBAC). Both strongly preferred.
• Demonstrated ownership of dimensional data model design — Star Schema, Snowflake Schema, or Data Vault — not just implementation within pre-defined frameworks.
• Hands-on BI/visualization experience — Power BI strongly preferred (DAX, star schema for BI, RLS, dashboard development). Tableau acceptable.
• Strong Python and PySpark skills for large-scale data processing.
• Solid SQL expertise — advanced queries, stored procedures, performance tuning, execution plan analysis.
• Cloud platform experience: Azure (ADF, ADLS, Synapse) and/or AWS (S3, Glue, EMR, Redshift).

Good to Have:

• Experience with streaming platforms such as Apache Kafka or Kinesis.
• Familiarity with dbt for transformation layer management.
• Microsoft Fabric or Azure Synapse Analytics exposure.
• Relevant certifications: DP-203, Databricks Data Engineer Associate, Snowflake SnowPro Core, or Power BI PL-300.
• Multi-cloud experience across Azure and AWS.

Benefits: At Calfus, we value our employees and offer a strong benefits package. This includes medical, group, and parental insurance, coupled with gratuity and provident fund options. Further, we support employee wellness and provide birthday leave as a valued benefit.

Calfus is an Equal Opportunity Employer. We believe diversity drives innovation. We’re committed to creating an inclusive workplace where everyone—regardless of background, identity, or experience—has the opportunity to thrive. We welcome all applicants!