SSC HR Solutions

Data Engineer

• Own the end-to-end data platform architecture and define technical standards for data engineering teams
• Design and implement batch and real-time/streaming data architectures
• Define Lakehouse architecture, open table formats, and query engine solutions
• Design real-time data serving and OLAP solutions
• Establish standards for ETL pipelines, schema evolution, data quality, data lineage, and access control
• Design scalable, cost-effective, and multi-tenant data platforms
• Work with self-managed and on-premises infrastructure
• Collaborate with data engineering and technical teams to ensure architecture standards are properly implemented

Senior Technical Data Architect – Telco, Banking

• Build and run large scale batch data pipelines.
• Keep data processing jobs fast and affordable as data volumes grow.
• Build and manage large scale Apache Spark data processing jobs.
• Perform performance tuning to maintain efficient and reliable pipeline execution.
• Support data modelling for analytics.
• Organise data in a lakehouse for downstream use.
• Handle automated scheduling, monitoring, and data quality checks.
• Work with platform and product teams on end to end data flows.

Senior Data Engineer, Spark

Frontend Engineer

• Build and maintain complex enterprise web applications using React and TypeScript.
• Develop interactive dashboards, data visualizations, workflow builders, and node-based interfaces.
• Design and implement scalable frontend architecture for large and complex applications.
• Manage application state using Redux Toolkit, Zustand, or similar state management solutions.
• Build and maintain reusable component libraries and shared UI patterns.
• Implement design systems in collaboration with UI/UX designers.
• Develop efficient data-heavy interfaces for large datasets and complex screens.
• Optimize frontend performance for large lists, complex visualizations, and data-intensive workflows.
• Use D3 or similar visualization libraries to create interactive data experiences.
• Build reusable frontend patterns to improve application consistency and development efficiency.
• Implement responsive and accessible user interfaces following established accessibility standards.
• Write and maintain unit, integration, and end-to-end tests.
• Use testing tools such as Jest, Playwright, Cypress, or equivalent frameworks.
• Collaborate with backend engineers to integrate APIs and manage asynchronous data flows.
• Identify and resolve frontend performance, usability, and reliability issues.
• Participate in code reviews and contribute to frontend engineering standards and best practices.
• Help evolve frontend architecture as the product grows in scale and complexity.

Senior Frontend Engineer, React, TypeScript

Marketing

• Own and develop CVM and marketing analytics for the banking industry
• Design and measure banking marketing campaigns end-to-end
• Apply customer segmentation, cross-sell modelling, campaign eligibility, uplift, and incrementality measurement
• Support banking commercial objectives including product holding and cross-sell, deposit and balance growth, lending and card propensity, and new-to-bank acquisition
• Own CVM use cases including cross-sell, card activation and usage, balance growth, silent attrition, and lifecycle and transaction triggers
• Design control groups and measure incrementality
• Apply marketing eligibility and regulatory requirements, including suitability, affordability, credit gating, and auditability
• Define customer attributes and segments in collaboration with Engineering teams

Senior CVM and Marketing Analysis, Banking Industry

Description
Builds and runs large scale batch data pipelines, keeping jobs fast and affordable as data volumes grow. Works with Apache Spark to build and manage large scale data processing jobs, including performance tuning to maintain efficient and reliable pipeline execution. Supports data modelling for analytics and organises data in a lakehouse so it is usable downstream. Handles automated scheduling, monitoring, and data quality checks, while working with platform and product teams on end to end data flows.

Senior Data Engineer (Spark)

Senior Technical Data Architect
We are looking for a Senior Technical Data Architect to own the technical data architecture end to end, defining how data is ingested, stored, processed, served, and governed across the data platform.
The role is responsible for setting the technical standards and architectural direction that data engineering teams build against, with a strong focus on scalability, reliability, performance, data quality, and maintainability.
Key Responsibilities:

- Own the end-to-end technical architecture of the data platform.

- Define how data is ingested, stored, processed, transformed, served, and governed.

- Design and evolve scalable batch and streaming data architectures.

- Define lakehouse architectures using open table formats and appropriate query engines.

- Design real-time data serving and OLAP architectures for analytical workloads.

- Establish standards for data pipelines, schema evolution, data quality, lineage, and access control.

- Define architectural patterns for ETL/ELT workflows and data processing.

- Guide the use of technologies such as Spark, Kafka, Airflow, and SQL across the data platform.

- Design data platforms that support scale, performance, cost efficiency, and multi-tenancy.

- Evaluate architectural trade-offs and clearly communicate technical decisions.

- Establish reusable architecture patterns and engineering standards across data teams.

- Support the architecture of multiple deployments of the same data platform for different clients.

- Contribute to data mesh and data product operating models where appropriate.

- Work closely with data engineers and technical stakeholders to ensure architectural standards are consistently implemented.

- Identify architectural bottlenecks and drive improvements in reliability, scalability, and data processing efficiency.

- Define technical approaches for schema management, data governance, and data lifecycle management.

- Design architectures suitable for self-managed and on-premises infrastructure environments.

Senior Data Architect - CVM & Marketing Analytics

Machine Learning Engineer

Description
Owns the predictive customer scores that ship with the product: churn, propensity, lifetime value, spend intent, and response scoring, from training through to monitoring. Applies machine learning and tabular predictive modelling to customer data, building and managing production models that support customer prediction and scoring. Works across the full model lifecycle, including model training, deployment, retraining, monitoring, evaluation, and calibration within a self-managed data platform environment. Supports predictive use cases such as churn, propensity, lifetime value, spend intent, and response scoring, with a focus on models running in production.

Senior ML Engineer

• Own and define the end-to-end telco data model, covering source systems, canonical entities, relationships, attributes, and business definitions
• Lead data discovery and requirements workshops with client stakeholders across business and technology teams
• Map data from BSS and OSS source systems into a canonical data model
• Define relationships between Party, Customer, Account, Subscriber, Service, Product, and Resource entities, including complex B2B, household, and family-plan scenarios
• Design and document identity resolution rules around MSISDN, customer, subscriber, SIM, and account relationships
• Address telecom-specific identity scenarios including number portability, SIM swaps, and number recycling
• Work with telecom usage and charging data, including CDRs/xDRs, mediation, rating, charging, and billing structures
• Develop detailed data mapping specifications, transformation rules, data definitions, and implementation-ready documentation
• Collaborate with data engineering, architecture, product, BSS/OSS, and business teams to ensure the data model can be implemented effectively
• Support the design and implementation of Customer Data Platforms, Single Customer View, and customer 360 solutions
• Ensure data architecture aligns with relevant telecom standards and enterprise data governance requirements

Senior Telecom Data Architect – BSS/OSS, Customer Data

Backend Engineer

• Design and operate production Kubernetes clusters on bare metal, private cloud, and on-premises infrastructure.
• Own Kubernetes platform architecture from cluster design through deployment and ongoing operations.
• Define reference Kubernetes deployments installable and operable by client infrastructure teams.
• Design and manage CNI networking, CSI integrations, persistent storage, ingress, and load balancing.
• Manage etcd backup, recovery, health monitoring, and disaster recovery procedures.
• Build and maintain deployment workflows using Helm and GitOps tools such as ArgoCD or Flux.
• Use Terraform to automate infrastructure and Kubernetes platform provisioning where appropriate.
• Design and implement air-gapped and restricted-network Kubernetes deployments.
• Manage container image mirroring, private registries, and offline installation processes.
• Deploy and operate stateful workloads including Kafka, Flink, Spark, databases, and their operators.
• Design highly available Kubernetes platforms for business-critical workloads.
• Implement security hardening for regulated and security-sensitive environments.
• Define and enforce RBAC, network policies, secrets management, and cluster security policies.
• Plan cluster capacity, node resources, scaling strategies, and workload placement.
• Design and execute disaster recovery and business continuity approaches.
• Plan and perform Kubernetes upgrades with minimal or zero downtime.
• Support GPU scheduling and node management for AI and data-intensive workloads.
• Work with client infrastructure and operations teams during deployment, handover, and operational readiness.
• Participate in security reviews and technical architecture discussions.
• Troubleshoot complex networking, storage, scheduling, and cluster-level production issues.
• Establish operational standards, runbooks, monitoring requirements, and troubleshooting procedures.

Senior Kubernetes Engineer

• Design, develop, and maintain high-performance backend services and REST APIs using Java and Spring Boot
• Build and maintain scalable microservices with a focus on reliability, maintainability, and performance
• Develop event-driven architectures and asynchronous processing flows using Kafka or similar streaming platforms
• Design and optimize relational database solutions, including PostgreSQL, with a strong focus on query performance and efficient data handling
• Implement and optimize caching solutions using Redis, Ignite, or similar technologies
• Develop and integrate gRPC services for efficient communication between distributed services
• Design and implement multi-tenant backend services, ensuring appropriate tenant isolation, scalability, and security
• Containerize and deploy services using Docker and Kubernetes
• Contribute to CI/CD pipelines and automated deployment processes
• Write unit, integration, and performance tests to ensure application reliability and quality
• Apply appropriate design patterns, clean code principles, and software engineering best practices
• Participate in code reviews, technical discussions, and architectural decisions
• Perform performance profiling, troubleshooting, and tuning to optimize services under heavy workloads
• Monitor and analyze application performance, database queries, resource utilization, and system bottlenecks
• Collaborate with other engineers and teams to design and deliver reliable distributed systems

Senior Java Backend Engineer – Spring Boot, Microservices, Kafka

Solutions Engineer

• Own the end-to-end technical design of applications, including services, APIs, integrations, and system communication.
• Define and guide application architecture using Java, Spring Boot, REST APIs, microservices, Docker, and Kubernetes.
• Design APIs and integrations across multiple systems.
• Guide engineering teams on architectural and technical design decisions.
• Review implemented solutions to ensure alignment with the defined architecture.
• Maintain clear and practical architecture documentation that can be easily followed by new engineers.
• Design and support event-driven architectures using Kafka.
• Work with self-managed infrastructure rather than managed cloud services.
• Contribute with a strong hands-on background across both backend and frontend development.
• Apply solution/application architecture experience within a product-company environment.

Senior Solution Architect – Application

Digital Marketing

• Own audience activation across digital and omnichannel, including how audiences are packaged and made usable by advertisers and how performance is measured back into the data model
• Apply digital marketing, adtech, or audience activation experience on the brand, agency, publisher, or platform side
• Run campaigns hands-on across major digital and audience platforms, including custom and matched audience uploads
• Activate first-party data through onboarding, audience syncs, identifiers, and match rate economics
• Orchestrate omnichannel campaigns by coordinating digital with CRM and direct channels
• Manage attribution, test and control design, and incrementality

Senior AdTech, Digital Marketing

• Own the customer data use case library and define the commercial purpose of each use case
• Translate business objectives into customer attributes, segments, and actionable data requirements
• Design and define customer segmentation strategies for targeted campaigns and customer lifecycle management
• Own use cases end to end, including churn, retention, dormancy, win-back, upsell, tariff migration, and device upgrade
• Define customer base management strategies and identify high-value customer segments
• Design campaign targeting criteria and ensure segments are practical and commercially actionable
• Work with customer data and campaign management platforms to build and activate segments
• Define measurement frameworks for campaign performance, customer response, and incremental impact
• Design control groups and measurement approaches to evaluate campaign incrementality
• Analyze customer behavior and identify opportunities for retention, cross-sell, upsell, and reactivation
• Work closely with engineering and data teams to specify required customer attributes, segment logic, and data definitions
• Challenge unnecessary segmentation and prioritize use cases that can be practically activated and measured
• Ensure customer segments have clear business definitions, eligibility criteria, and measurable outcomes
• Support the development of reusable customer data and campaign use cases across banking products and customer journeys
• Translate analytical insights into clear requirements for campaign and customer data teams

Senior Data Architect – Banking

Business Analyst

• Maintain market, competitive, and demand intelligence informing commercial strategy, pricing, and product decisions.
• Provide market sizing, competitive positioning, and account intelligence for major tender development.
• Track competitor activity, pricing signals, and market shifts, translating them into actionable briefs.
• Build and maintain dashboards and standard reporting for the Strategic Marketing & Technology organization.
• Own win/loss reporting and identify patterns behind wins and losses.
• Deliver ad hoc analysis for commercial and leadership decisions.
• Perform data analysis, extraction, and visualization for reporting.
• Own CRM data integrity, pipeline hygiene, and adoption support across the commercial team.
• Manage lead and opportunity data, including capture, routing, and tracking.
• Maintain the commercial data foundation feeding reporting and AI agents.
• Operate the market-signal and account-intelligence agent and support pricing and tender agents.

Commercial Intelligence and Business Analyst – Oil and Gas

• Decide which customer attributes and segments are worth building
• Own the Telco use case library
• Define what each customer segment means commercially and how its value is measured
• Design and measure customer campaigns
• Build and use customer segments in a real customer data or campaign environment
• Own CVM use cases end to end, including churn, retention, dormancy, win-back, upsell, tariff migration, and device upgrade
• Prove campaign impact through control design, uplift, and incrementality measurement
• Work with engineering teams to specify customer attributes and segments
• Work with a Customer Data Platform or campaign management platform