Data Engineer

Primary Skill: PySpark, Hive, Python, SQL, Hadoop

Secondary: Unix, Agile, Base support

Experience: 5 to 10 years

Roles & Responsibilities

5 to 10 years of experience in Data Engineering, Software Engineering, or related technical discipline.

Strong proficiency in Python, and SQL for advanced data transformations.

Hands-on experience designing and building ETL/ELT pipelines, data ingestion processes, and distributed data processing jobs.

Practical experience working with distributed data tools such as Apache Spark, Databricks, or Hadoop ecosystems.

Experience building and managing datasets in relational and/or cloud based data platforms (Teradata, Snowflake, SQL Server, Azure/AWS/GCP).

Solid understanding of data modeling, metadata, data quality controls, data lineage, and secure data management.

Experience contributing to automated test suites, analyzing test failures, and supporting test-driven development.

Knowledge of CI/CD pipelines, version control (Git), and automated deployment practices.

Experience adhering to enterprise data governance, compliance, and operational risk frameworks.

Ability to troubleshoot pipeline issues, performance bottlenecks, and data discrepancies.

Strong communication skills and ability to collaborate across engineering, product, and business teams.

Experience implementing monitoring and observability for data pipelines (logs, metrics, health checks).

Advanced experience with performance tuning of SQL, Spark, or distributed data workflows.

Knowledge of data security practices (encryption, masking, PII handling).

Experience supporting analytical workloads, BI tools, or data science teams.

Prior experience in a financial institution or other regulated industry

A Data Engineer is essential to implement efficient data flows, enforce data management standards, enhance data quality, and support continuous delivery and release cycles. Without this role, the project risks delays in data readiness, gaps in data compliance, reduced quality of analytical outputs, and inability to support downstream systems effectively.

Client is mainly looking for Production support ole to be able to root cause analysis of prod issues across different tech stack that we have(Hadoop, Oracle and MongoDB).Autosys knowledge, Analyze and fix performance issues, Dev work related to App Gov and other mandates., Agile Programming Methodologies, Amazon Web Services (AWS), Analysis Skills, Apache Hadoop, Apache Hive, Apache Spark, Business Intelligence Software, CA Workload Automation AE (AutoSys Edition), Cloud Computing, Communication Skills, Continuous Deployment/Delivery, Continuous Integration, Cryptography, Data Management, Data Modeling, Data Processing, Data Quality, Data Science, Data Sets, Database Extract Transform and Load (ETL), Ecosystems, Failure Analysis, GCP (Good Clinical Practices), Git, Identify Issues, Information/Data Security (InfoSec), Metadata, Metrics, Microsoft SQL Server, Microsoft Windows Azure, MongoDB, NCR Teradata, OLE (fka Object Linking and Embedding), Oracle, Performance Analysis, Performance Tuning/Optimization, Problem Solving Skills, Product Support, Production Support, Python Programming/Scripting Language, Risk, Root Cause Analysis, SQL (Structured Query Language), Snowflake Schema, Software Engineering, Source Code/Configuration Management (SCM), Team Player, Test Automation, Test Driven Development (TDD), Test Suite, Unix Operating Systems