Senior Data Engineer

JOB DESCRIPTION Data Engineer Designation – Data Engineer Experience – 5+ Years Location Mumbai (onsite)

Job Summary: We are seeking a highly skilled Data Engineer with deep expertise in Apache Kafka integration with Databricks, structured streaming, and large-scale data pipeline design using the Medallion Architecture. The ideal candidate will demonstrate strong hands-on experience in building and optimizing real-time and batch pipelines, and will be expected to solve real coding problems during the interview. Job Description: • Design, develop, and maintain real-time and batch data pipelines in Databricks. • Integrate Apache Kafka with Databricks using Structured Streaming. • Implement robust data ingestion frameworks using Databricks Autoloader. • Build and maintain Medallion Architecture pipelines across Bronze, Silver, and Gold layers. • Implement checkpointing, output modes, and appropriate processing modes in structured streaming jobs. • Design and implement Change Data Capture (CDC) workflows and Slowly Changing Dimensions (SCD) Type 1 and Type 2 logic. • Develop reusable components for merge/upsert operations and window functionbased transformations. • Handle large volumes of data efficiently through proper partitioning, caching, and cluster tuning techniques. • Collaborate with cross-functional teams to ensure data availability, reliability, and consistency. Must Have: • Apache Kafka: Integration, topic management, schema registry (Avro/JSON). • Databricks & Spark Structured Streaming: o Processing Modes: Append, Update, Complete o Output Modes: Memory, Console, File, Kafka, Delta o Checkpointing and fault tolerance • Databricks Autoloader: Schema inference, schema evolution, incremental loads. • Medallion Architecture implementation expertise. • Performance Optimization: o Data partitioning strategies o Caching and persistence o Adaptive query execution and cluster configuration tuning • SQL & Spark SQL: Proficiency in writing efficient queries and transformations. • Data Governance: Schema enforcement, data quality checks, and monitoring. • Good to Have: • Strong coding skills in Python and PySpark. • Experience working in CI/CD environments for data pipelines. • Exposure to cloud platforms (AWS/Azure/GCP). • Understanding of Delta Lake, time travel, and data versioning. • Familiarity with orchestration tools like Airflow or Azure Data Factory.

Mandatory Hands-on Coding Assessment (During Interview): Candidates will be required to demonstrate hands-on proficiency in the following areas:

1. Window Functions: o Implement logic using ROW_NUMBER, RANK, and DENSE_RANK in Spark. o Use cases such as deduplication, ranking within groups.

2. Merge/Upsert Logic: o Write PySpark code to perform MERGE operations in Delta Lake.

3. SCD Implementation: o SCD Type 1: Overwriting existing records. o SCD Type 2: Versioning records with effective start/end dates or is_current flags.

4. CDC (Change Data Capture): o Capture and process changes using techniques such as: ▪ Comparison with previous snapshots ▪ Using audit columns or timestamps ▪ Kafka-based event-driven ingestion