Bigdata developer

role Name: /Sr. Bigdata developer/
Location: Toronto 4 days a week on site F2F interview required

JOB DESCRIPTION:
"Role Overview

We are looking for a Senior Backend Developer with 5+ years of experience in big data engineering, API integration, and AI-assisted development. The ideal candidate will design, build, and maintain scalable data pipelines and backend systems in a enterprise environment.

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Key Responsibilities

Big Data & Spark

Design and develop Spark-Scala applications for large-scale data processing on Hadoop/CDP clusters
Build and optimize ETL/ELT pipelines using Spark DataFrames, Datasets and Spark SQL
Tune Spark jobs for performance (partitioning, caching, broadcast joins, shuffle optimization)
Migrate Spark 2 applications to Spark 3 on Cloudera CDP platforms
Work with Parquet, ORC, Avro file formats on HDFS
SQL & Data Engineering

Write complex HiveQL / Spark SQL queries including window functions, CTEs, subqueries and aggregations
Design and maintain Hive external/managed tables and partitioned datasets
Optimize slow-running queries and resolve correlated subquery issues
Work with HDFS encryption zones and data governance requirements
Unix / Shell Scripting

Develop and maintain bash shell scripts for job orchestration and automation
Handle error management, return codes, logging and alerting in shell scripts
Manage HDFS operations (hdfs Client commands), file transfers, and data validation
Manage Kerberos authentication (kinit, keytab handling)
API Extraction & Integration

Build scripts and pipelines to extract data from REST APIs using curl and Python
Handle OAuth2 token generation, bearer token refresh and API health checks
Parse and process JSON API responses and load into HDFS/Hive
Manage pagination, error handling and retry logic for API calls
Work with enterprise API gateways and URL parameter construction
AI & Copilot Capabilities

Leverage GitHub Copilot / AI coding assistants to accelerate development
Use AI tools for code review, SQL generation, script debugging and documentation
Contribute to AI-assisted data quality and anomaly detection pipelines
Explore and implement LLM-based automation for repetitive data engineering tasks
Scheduling & Orchestration

Schedule and manage jobs using AAP (Ansible Automation Platform) / Control-M / cron
Build and maintain Ansible playbooks for automated deployments
Manage deployment pipelines including artifact versioning, Vault secret injection and environment-specific configuration
Monitor job health, handle failures and implement alerting

Nice to Have

Experience with Cloudera CDP (7.x) and migration from HDP
Knowledge of Kerberos, Vault, HDFS encryption zones
Familiarity with CI/CD pipelines (Helios, GitHub Actions)
Experience with MSSQL / JDBC connectivity from Spark
Understanding of AML / Financial regulatory data domains"