{"id":3983994,"url":"https://alion.io/job/gmx-it-services-fzco-lead-data-engineer","title":"Lead Data Engineer","company":{"id":4529282,"name":"Gmx It Services-Fzco","domain":null,"url":"https://alion.io/company/gmx-it-services-fzco","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Addis Ababa, Ethiopia"],"countries":["ET"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Kafka","optional":false},{"name":"CI/CD","optional":false},{"name":"ClickHouse","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Feature Store","optional":false},{"name":"GitLab CI","optional":false},{"name":"Grafana","optional":false},{"name":"Kubernetes","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Scala","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-11-09T16:49:32Z","employer_posted_date":null,"last_verified_at":"2026-11-09T16:49:32Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"About the role\nThe Lead Data Engineer is responsible for the bank's target data platform and the data engineering team. The scope includes the ingestion, processing, storage, and serving layers; batch and streaming pipelines; data contracts; governance; and SLAs for data freshness, completeness, and correctness.\nResponsibilities\nDesign and evolve the target lakehouse/DWH architecture, from ingestion to serving.\nOwn the architecture of batch and streaming pipelines and the standards for their implementation.\nBuild streaming-first integrations with banking domains using Kafka.\nImplement change data capture (CDC) from the core banking system (CBS) and other operational systems, with controls for completeness, ordering, and redelivery.\nDevelop the Data Lake, ClickHouse, analytical data marts, and the serving layer for BI, ML, and AI agents.\nDesign real-time data marts and streams for fraud prevention and risk management.\nImplement data contracts, a schema registry, schema compatibility rules, and a change management process.\nEnsure data lineage, a data catalog, data quality monitoring, and classification of personally identifiable information (PII).\nDefine SLAs/SLOs for data freshness, completeness, and correctness, and coordinate the response to breaches.\nDevelop and maintain ETL/ELT pipelines using Airflow, dbt, Spark, and Kafka.\nOptimize performance, storage and compute costs, the data lifecycle, and data consumption.\nImplement CI/CD, automated testing, and observability for data pipelines.\nDevelop data mart architecture for analytics, feature stores, ML/GenAI, and RAG use cases.\nManage the data engineering team, including planning, hiring, performance reviews, professional development, and technical mentoring.\nCollaborate with Analytics, ML, Product, Engineering, Security, and banking domain owners.\nTechnology context\nKafka, schema registry, and CDC.\nAirflow, dbt, and Spark.\nClickHouse MergeTree, S3 Data Lake, and lakehouse/DWH.\nSQL, Python, and/or Scala.\nKubernetes and GitLab CI/CD.\nOpenTelemetry, Prometheus, Grafana, and dedicated data quality monitoring.\nData contracts, lineage, catalogs, and PII classification.\nPostgreSQL and other operational data sources from banking domains.\nRequirements\nWhat we are looking for\nAt least three years of hands-on experience in Data Engineering and a proven track record of technical leadership.\nExperience designing production data platforms, from ingestion through data marts and serving.\nExcellent SQL and Python skills.\nHands-on experience with Apache Airflow, Spark, and Kafka.\nExperience with batch and streaming processing, CDC, and handling late and duplicate events.\nAn understanding of lakehouse/DWH approaches, data modeling, and data platform layers.\nPractical experience with data contracts, schema evolution, and schema registries.\nExperience implementing CI/CD, testing, and monitoring for data pipelines.\nAn understanding of data governance, lineage, catalogs, data quality, and PII protection.\nExperience managing a team or a sustained track record of technical leadership across multiple data projects.\nEnglish B2 level or above.\nNice to have\nExperience with ClickHouse MergeTree and optimizing high-load analytical queries.\nPractical experience with MLOps, feature stores, and collaborative development with ML engineers.\nExperience with production GenAI/RAG pipelines and an understanding of their data and observability requirements.\nExperience building real-time data streams for fraud prevention, risk management, or other critical decisions.","description_format":"text","description_chars":3542,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Professional development"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-11-09T20:57:15Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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