Pyspark Engineer at Citigroup in DLF Cybercity 12B

Job description

Job Description We are seeking an experienced and results-driven PySpark / Big Data Engineer to design, develop, and maintain large-scale batch and real-time data pipelines. In this role, you will be responsible for transforming complex raw data into actionable analytical data models, optimizing distributed computing workloads, and ensuring data integrity, scalability, and security across enterprise data platforms. The ideal candidate possesses deep expertise in Apache Spark architecture, advanced Python programming, distributed systems optimization, modern cloud platforms, and data warehousing/lakehouse paradigms. Key Responsibilities Data Pipeline Design & Development Architect, build, and maintain robust, scalable ETL/ELT pipelines using PySpark and Python to ingest data from diverse batch and streaming sources (Kafka, relational databases, REST APIs, Object Storage). Design and implement Lakehouse architectures using formats such as Delta Lake, Apache Iceberg, or Apache Hudi . Build reusable frameworks and data ingestion libraries to standardize enterprise data processing. Spark Performance Tuning & Optimization Diagnose and tune Spark application performance, managing memory allocation, partitions, execution plans, broadcast joins, data skews, and garbage collection. Optimize query execution plans using Catalyst Optimizer and Tungsten execution engine. Monitor and troubleshoot production Spark jobs across cluster management environments (YARN, Kubernetes, or Databricks). Data Modeling & Architecture Collaborate with Data Architects to design dimensional (Star/Snowflake), normalized, and Data Vault schemas optimized for analytical workloads. Ensure efficient data storage, partitioning strategies, and file format selection (Parquet, ORC, Avro). Orchestration, CI/CD & DevOps Schedule and orchestrate complex workflow DAGs using workflow orchestrators such as Apache Airflow , Prefect, or Dagster. Implement CI/CD pipelines (Git, Jenkins, GitLab CI, or GitHub Actions) for automated testing, packaging, and deployment of data pipelines. Write unit tests, integration tests, and data validation routines (e.g., Great Expectations, PyTest). Governance, Security & Quality Implement enterprise data governance, lineage tracking, metadata management, and data quality frameworks. Ensure strict compliance with data security, masking, tokenization, RBAC, and regulatory privacy standards (e.g., GDPR, CCPA). Required Technical Qualifications & Skills Core Technologies - Apache Spark, PySpark (Spark SQL, DataFrames, Datasets, Structured Streaming), Python 3.x Database & Querying - Advanced SQL, Query optimization, Indexing, Window functions Data Lake & Lakehouse - Delta Lake, Apache Iceberg, Apache Hudi, Parquet, Avro, ORC Workflow & Orchestration - Apache Airflow, Luigi, Prefect, or Databricks Workflows Cloud Platforms - AWS (EMR, Glue, S3, Redshift), Azure (Synapse, ADLS, HDInsight), or GCP (Dataproc, BigQuery, GCS) Streaming Technologies - Apache Kafka, Spark Structured Streaming, Event Hubs, or Kinesis Key Competencies & Experience Distributed Computing Fundamentals: In-depth knowledge of Spark internals (RDD, Catalyst Optimizer, DAG execution, Shuffling, Narrow vs. Wide dependencies). Production Experience: Minimum 4+ years of hands-on experience building and deploying production-grade PySpark applications at enterprise scale. Problem Solving: Proven track record in debugging memory bottlenecks, Out-Of-Memory (OOM) errors, and resolving data skew issues. Data Engineering Best Practices: Experience implementing modular code architectures, logging, monitoring (Prometheus, Grafana, CloudWatch), and alerting. 5. Preferred Qualifications (Nice-to-Have) Hands-on experience with Databricks platform (Delta Lake, Unity Catalog, Photon engine, MLflow). Familiarity with Scala/Java in addition to Python. Knowledge of DataOps and Infrastructure as Code ( Terraform , CloudFormation). Experience with modern analytical data warehouses (Snowflake, Google B