Jobs at Techsa FZC

Own the end-to-end architecture of a sovereign, on-premise, telco-native customer data platform running on Kubernetes and streaming infrastructure. Define distributed-system topology, Flink and Kafka architectures, state placement, lakehouse storage, identity resolution, governance, security, observability, multi-tenancy, resiliency, sizing, and artifact activation. Provide architectural standards, design documentation, platform service boundaries, and technical guidance for engineering teams building high-throughput, low-latency systems.

Own end-to-end application solution architecture, including services, APIs, integrations, and system interactions. Guide engineering teams on design decisions, review implementations, translate business requirements into scalable solutions, and address performance, reliability, data quality, usability, and operational risks. Collaborate across engineering, product, data, design, and business teams while maintaining architecture standards and documentation.

Develop scalable full-stack applications across the software lifecycle. Responsibilities include designing client- and server-side architecture, building Flutter interfaces, developing .NET back-end systems and APIs, optimizing databases, testing, debugging, implementing security measures, participating in code reviews, and maintaining technical documentation. The role collaborates within Agile/Scrum teams and may involve cloud deployment, CI/CD, and containerization.

Owns and evolves the telecommunications data model from source systems through canonical entities, identity rules, and attribute definitions. Translates stakeholder requirements into scalable, production-ready data solutions, maps BSS and OSS sources, and produces implementable specifications. Applies expertise in TM Forum SID, eTOM, 3GPP charging, CDR/xDR, identity resolution, SQL, and data quality. Collaborates with engineering, product, design, and business teams while addressing performance, scalability, reliability, and integration risks.

Own Kubernetes platforms across bare-metal, private-cloud, and sovereign environments. Design reference deployments for client operations teams, build production-ready capabilities, and guide architecture, reliability, security, scalability, and continuous improvement. Operate air-gapped clusters and stateful workloads, including Kafka, Flink, Spark, and databases. Implement storage, networking, ingress, certificates, etcd recovery, policy enforcement, disaster recovery, capacity planning, high availability, and zero-downtime upgrades while collaborating across engineering, product, data, and business teams.

Build and operate high-scale batch and near-real-time data pipelines on self-managed infrastructure. Design analytical data systems using Apache Spark for batch and streaming workloads, tune performance, manage distributed clusters, integrate Kafka and query engines, and support ETL, data warehousing, lakehouse, orchestration, governance, and low-latency analytical systems.

Supports capital expenditure operations by processing invoices, creating approval and system entries, coordinating purchase orders, tracking budgets, resolving vendor and transaction issues, supporting monthly accruals, and maintaining accurate records. The role requires cross-functional coordination with vendors, accounting, IT, project managers, and budget teams while following up on outstanding transactions through completion.

Owns telco customer value management and marketing analytics, including segmentation, campaign design, use-case development, and incrementality measurement. Defines commercially meaningful customer segments, manages telco use cases such as churn, retention, win-back, upsell, and tariff migration, and partners with engineering, product, data, and business teams to deliver scalable solutions. Requires deep knowledge of telco commercial drivers and hands-on SQL experience.

Owns end-to-end technical data architecture, including data ingestion, storage, processing, serving, governance, and platform standards. Designs scalable batch and streaming systems using lakehouse architectures, open table formats, query engines, real-time serving, and OLAP stores. Partners with engineering, product, data, and business teams to deliver reliable production capabilities, manage data quality and lineage, and address performance, scalability, cost, security, and multi-tenancy tradeoffs. Telco or banking experience is required.

Own production machine learning solutions for predictive customer scores, including churn, propensity, lifetime value, spend intent, and response models. Responsibilities include requirements, architecture, implementation, deployment, retraining, monitoring, drift detection, and continuous improvement. Collaborate with engineering, product, data, design, and business stakeholders to build scalable, reliable capabilities within a data platform. The role requires deep experience with tabular customer modeling and production MLOps in telco or banking.

Design, build, and operate high-scale, low-latency streaming data pipelines on self-managed infrastructure. Own Apache Flink systems end to end, including state management, checkpointing, fault tolerance, event-time processing, backpressure tuning, deployment, and upgrades. Integrate messaging, query engines, ETL tools, data warehouses, analytical stores, lakehouse formats, orchestration systems, and governance platforms. Develop reliable, high-quality code and support real-time data workloads using on-premises infrastructure.

Drive B2B sales for a digital procurement platform by generating leads, prospecting corporate buyers and suppliers, managing the full sales cycle, delivering platform demos, maintaining qualified pipelines, and achieving sales targets. The role involves collaboration with marketing and product teams, site visits, and participation in industry events.

Own quality engineering for a high-scale, low-latency streaming Customer Data Platform. Build and maintain API, UI, end-to-end, data-pipeline, streaming, performance, governance, resilience, and privacy test automation. Validate SLOs, event processing, identity resolution, lakehouse data, tenant isolation, and recovery behavior. Integrate evidence-producing tests into CI/CD with promotion gates, manage synthetic test data, diagnose flaky tests, and produce auditable results tied to artifact versions.

Design and build scalable Java and Spring Boot backend services, REST APIs, microservices, and event-driven systems. Develop high-throughput data processing with Kafka, integrate and optimize PostgreSQL, MySQL, MongoDB, and Redis databases, and collaborate with cross-functional teams. Write unit and integration tests, apply clean coding practices, and containerize and deploy services using Docker, Kubernetes, and CI/CD pipelines.

Design intuitive, accessible web and mobile product experiences by translating user and business needs into wireframes, prototypes, and high-fidelity designs. Collaborate with product, engineering, and design teams; maintain scalable design systems; conduct user research and usability testing; iterate using feedback and data; and communicate design decisions to stakeholders.

Own software projects from planning through release by coordinating developers and QA, maintaining delivery plans, tracking progress, resolving blockers, managing scope changes, coordinating release readiness, and communicating status, risks, and decisions to stakeholders. The role requires strong project management, organization, follow-up, communication, and software development project delivery experience.

Owns end-to-end ML and AI systems, including data pipelines, model training, LLM applications, multi-agent orchestration, RAG, inference infrastructure, MLOps, and real-time scoring. Builds traditional predictive models and streaming or batch feature pipelines using Flink and Spark. Optimizes inference for high-scale workloads, develops AI operators, and partners with data and platform teams. Requires production experience with LLM applications, agent systems, RAG evaluation, vector databases, MLOps, and high-throughput inference.

Owns audience activation across digital and omnichannel channels, including first-party data onboarding, audience taxonomy, synchronization, match-rate optimization, attribution, incrementality, and campaign execution. Partners with engineering, product, data, design, and business teams to deliver scalable, reliable platform capabilities. Contributes to architecture, quality, documentation, and continuous improvement while addressing data, performance, privacy, identity, and operational risks.

Own and deliver production AI capabilities over enterprise data, including retrieval, RAG, agents, tool calling, and natural language interfaces. Design scalable architectures, evaluation frameworks, regression suites, grounding checks, and guardrails. Collaborate with engineering, product, data, design, and business teams while addressing reliability, performance, data quality, and operational risks. The role requires strong Python and LLM engineering experience, with preferred expertise in self-hosted model serving, inference optimization, GPU management, fine-tuning, and Text-to-SQL.