Nitish Saxena

DevOps Engineer

Pune, Maharashtra, India5 yrs 11 mos experience

Key Highlights

Over 9 years of experience in Big Data and Cloud technologies.
Expert in building scalable data solutions for diverse industries.
Strong focus on performance tuning and cost optimization.

Stackforce AI infers this person is a Big Data Engineer with expertise in Fintech and Cloud solutions.

Contact

nitish.saxena@citi.com +919834545794 LinkedIn

Skills

Core Skills

Big DataCloud

Other Skills

AWSAkkaApache KafkaApache Spark StreamingApache ZooKeeperCDCCouchDBCouchbaseElastic SearchGemfireGitHBaseHadoopHiveHiveQL

About

Sr. Big Data Engineer | AWS | Scala | Java | Spark | Hadoop | Cloud Solutions | Remote-First With over 9 years of experience in Big Data and Cloud technologies, I specialise in building scalable, high-performance data solutions across domains like energy, banking, telecom, and finance. Currently working with Digit88 (client: Gridx), a leader in the digital energy space, where I contribute to developing robust data frameworks for forecasting, billing optimisation, and cost calibration using Apache Spark, Hadoop, and AWS cloud-native services. Previously, I served as a Senior Engineer at CitiBank, leading data-driven solutions on enterprise-scale financial platforms. Core Skills: * Big Data: Spark, Hadoop, Hive, HBase, Kafka, Flume, Oozie, NiFi * Programming: Scala, Java, Python, SQL, Shell Scripting * Cloud: AWS (EMR, S3, Glue, Lambda, Kinesis, Redshift), Azure, GCP * ETL & Data Pipelines: Airflow, Glue, Spark, Snowflake, dbt, Fivetran * Microservices & API: Akka, Spring Boot, Scala HTTP (Akka HTTP, Play) * DevOps: CI/CD, Jenkins, Git, Terraform * Security: Kerberos, SSL, IAM roles * Architecture: Real-time & batch processing, Delta Lake, Iceberg, system design What I bring: * Hands-on development and optimisation of large-scale data pipelines * Cloud-native architecture and automation using AWS services * Deep understanding of data modeling, governance, and analytics * Strong focus on performance tuning, cost optimisation, and security * Experience collaborating across cross-functional teams and domains Always eager to explore innovative data solutions that drive business value. Currently open to permanent remote opportunities globally. Let’s connect and collaborate!

Experience

5 yrs 11 mos

Total Experience

1 yr 11 mos

Average Tenure

Current Experience

Citi

Big Data Developer

Jun 2020 – Nov 2022 · 2 yrs 5 mos · India · Remote

Sr. Big Data Engineer
Pune, India | June 2019 – May 2024
Role Summary:
Worked on large-scale financial data processing systems, modernising batch ETL pipelines and enabling analytics across high-volume transactional data. Designed resilient, secure data workflows that supported regulatory reporting, customer insights, and business intelligence for Citi’s global operations.
Key Responsibilities:
Data Engineering & Pipelines
Developed scalable Spark-based batch and streaming jobs to ingest, transform, and enrich financial datasets.
Ingested data into Hadoop using Sqoop and integrated with Hive for warehousing and analytics.
Worked across multiple formats: AVRO, ORC, Parquet, CSV, XML, and JSON for structured/unstructured processing.
Real-Time & Streaming
Built Kafka-based streaming jobs with Spark Streaming for near real-time fraud detection and account activity insights.
Secured data pipelines using Kerberos, SSL, and SASL authentication.
Data Lake & Warehousing
Integrated HDFS with Hive, performing aggregations, windowing, and complex joins using HiveQL and Spark SQL.
Implemented optimised data models for high-performance financial reporting.
Cloud & Big Data Platform
Contributed to cloud migration and hybrid cloud architecture using AWS (S3, EMR, Redshift) for parallel compute and storage.
Optimised resource usage and reduced Spark job execution time by 30% with efficient partitioning and memory tuning.
Security & Compliance
Ensured compliance with enterprise-grade security policies and financial data regulations.
Handled PII data securely across storage, transit, and processing layers.
Tech Stack:
Languages: Scala, Java, Python, SQL
Big Data: Spark, Hadoop, Hive, HBase
Streaming: Kafka, Spark Streaming
Cloud: AWS (S3, EMR, Redshift)
Security: Kerberos, SSL/SASL
Tools: Sqoop, Oozie, Jenkins, Git