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1–3 jobs· newest first

Data Archival professionals
Diverse Lynx · Remote
$70–$75Remote · Contract · Senior
Lead Data Engineer
Mastercard · Pune, India
Senior
Big Data Engineering AVP
Citi · Pune, India
Full-time · Senior

+10K new in the last hour

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Data Archival professionals

Diverse Lynx · Posted Nov 5, 2026

Remote
Remote
Contract · Senior
$70–$75 per hour
5+ years

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Lead the design and implementation of an enterprise-grade archival solution on Platform 3 (Archon), covering end-to-end delivery from source discovery and retention-rule design to ETL build and operational handover.

Compensation

Max vendor rate 70-75/hr on w2

Responsibilities

Define end-to-end archival architecture on Platform 3 (Archon), including Archon Data Store (ADS), Archon ETL, and Archon Analyzer components.
Design Warm Archive, Cold Archive (de-identified), and Data Bunker tiers in line with retention, legal-hold, and WORM compliance requirements.
Translate business retention, privacy, and regulatory policies into archival rules, data classification, tier-mapping logic, and de-identification/tokenization strategies.
Produce reference architecture, data flow, integration, and security design artifacts aligned to the customer's System Security Plan (SSP).
Define retrieval patterns – predefined SQL/reports via the Archon UI, distributed query (Trino/Spark) and API-based access where required.

Requirements

Nice to have: mainframe
Nice to have: AS-400
Nice to have: MuleSoft
Nice to have: iPaaS
Nice to have: Archon

Skills

SQL relational database expertise DB2 Oracle SQL Server data warehousing data lakehouse ELT/ETL design Parquet ORC Iceberg Delta Azure cloud storage Blob ADLS Gen2 AWS S3 SSO SAML/OIDC encryption (AES-256, KMS)REST APIs CDC-based extraction data retention legal hold audit trails information-lifecycle management GLBA SOX HIPAA GDPR WORM compliance chain-of-custody controls Platform 3 (Archon)Azure AWS Trino Spark MuleSoft

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Lead Data Engineer

Mastercard · Posted Oct 29, 2026

Pune, Maharashtra, India
Senior

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Lead Data Engineer to design and build next-generation, cloud-native data platforms supporting Mastercard’s global data ecosystem.

Responsibilities

Design and build scalable batch and real-time data pipelines using Spark, Kafka, and (preferred) Apache Flink
Develop robust ETL/ELT frameworks for structured and unstructured data
Build and optimize data ingestion and transformation pipelines for Data Lakes and Data Warehouses
Implement stream processing solutions for near real-time use cases
Ensure data quality, lineage, observability, and governance across pipelines

Requirements

Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field

Skills

Java (JDK 8+)Python Go Object-Oriented Programming Design (OOP/OOAD)distributed systems multithreading

Big Data Engineering AVP

Citi · Posted Nov 3, 2026

Pune, Maharashtra, India
Full-time · Senior
10+ years

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Lead and develop big data and application systems for Citi’s technology team.

Responsibilities

Conduct feasibility studies, time and cost estimates, IT planning, risk technology, applications development, model development, and establish and implement new or revised applications systems and programs to meet specific business needs or user areas
Monitor and control all phases of development process and analysis, design, construction, testing, and implementation as well as provide user and operational support on applications to business users
Utilize in-depth specialty knowledge of applications development to analyze complex problems/issues, provide evaluation of business process, system process, and industry standards, and make evaluative judgement
Recommend and develop security measures in post implementation analysis of business usage to ensure successful system design and functionality
Consult with users/clients and other technology groups on issues, recommend advanced programming solutions, and install and assist customer exposure systems

Requirements

Bachelor’s degree/University degree or equivalent experience

Skills

Distributed Systems Real-Time Stream Processing

performance tuning

cloud-native data services

Apache Spark Streaming

ETL/ELT design patterns

pipeline architectures

dimensional modeling

star/snowflake schemas

cloud networking

security best practices

Data Warehouse platforms

problem-solving

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Big Data Engineering

ETL Pipeline Design

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