Pyspark Engineer

Citi; Chennai; Presencial; INR 900.000 - 1.500.000; Jornada completa

Descripción de la vacante

Citi in Chennai seeks an experienced PySpark/Big Data Engineer to design, develop, and maintain large-scale batch and real-time data pipelines. You will transform raw data into analytical models, optimize distributed workloads, and ensure data integrity, security, and scalability across enterprise platforms.

The role requires deep Spark expertise, Python proficiency, cloud familiarity, and experience with lakehouse patterns. Strong collaboration with data architects and DevOps teams is expected.

Formación

• Deep expertise in Spark architecture and PySpark
• Excellent Python programming and distributed systems
• Experience with data warehousing/lakehouse paradigms
• Cloud platform familiarity and data governance know-how

Responsabilidades

• Design, build, and maintain scalable ETL/ELT pipelines using PySpark and Python
• Tune Spark performance and optimize memory, partitions, and execution plans
• Model data with dimensional/normalized schemas and Lakehouse design
• Orchestrate workflows with Airflow/Prefect and implement CI/CD for pipelines
• Ensure data governance, lineage, and security compliance

Conocimientos

Spark internals

Production experience

Data engineering

Logging & monitoring

Python programming

Distributed computing

Educación

Bachelor's degree

Herramientas

Delta Lake

Apache Iceberg

Apache Hudi

Apache Airflow

Databricks

Terraform

Descripción del empleo

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.
• 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 BigQuery, Amazon Redshift).
• Relevant certifications: Databricks Certified Data Engineer Associate / Professional AWS Certified Data Engineer / Azure Data Engineer Associate (DP-203)CCA Spark and Hadoop Developer (or equivalent)
• Educational Requirements
• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology, Data Science, or a related quantitative field (or equivalent practical experience).

Primary : Py Spark, Python

Secondary : Bigdata, Abinitio, Unix, Oracle

Job Family Group

Technology

Job Family

Applications Development

Time Type

Full time

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