{"id":2188409,"url":"https://alion.io/job/sagacity-junior-data-engineer","title":"Junior Data Engineer","company":{"id":4376717,"name":"Sagacity","domain":"sagacity.com","url":"https://alion.io/company/sagacity-2","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":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":30000,"max":35000,"currency":"GBP","period":"year","gross":null,"usd_annual":46301},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Java","optional":false},{"name":"MySQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Databricks","optional":true}],"status":"closed","first_seen_at":"2026-10-09T15:29:31Z","employer_posted_date":null,"last_verified_at":"2026-10-28T22:50:15Z","board_verified":false,"closed_at":"2026-10-28T22:50:15Z","days_open":19,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":19},"description":"This role combines Helpdesk support with data engineering development. You will answer and triage incoming tickets, escalating them to the correct level of support, alongside supporting the design, maintenance, and optimisation of our data pipelines and data architecture for our implemented client products and services. Working closely with data analysts, data scientists, and other stakeholders, you will help ensure the efficient and reliable flow of data across the organisation.\nThis position includes Out of Hours work. Standard working hours are 6:00am to 2:30pm. There may be times where further Out of Hours coverage is needed,based on a rota; an Out of Hours Allowance is provided for this.\nHelpdesk & Support\nAnswer and triage incoming helpdesk tickets\nEscalate issues to the correct level of support\nMonitor the helpdesk queue and follow up on outstanding tickets and defects\nBuild exposure to client solution queries and investigations, bug fixes, and change controls\nData Pipeline Development\nAssist in designing, building, and maintaining scalable data pipelines\nImplement ETL (Extract, Transform, Load) processes to ingest data from various sources\nEnsure data quality, integrity, and reliability through thorough testing and validation\nDatabase Management\nSupport the management and optimisation of data storage solutions (e.g. SQL, NoSQL databases, data lakes)\nMonitor and maintain database performance and security\nData Integration\nCollaborate with data analysts and scientists to integrate and consolidate data from multiple sources\nDevelop APIs and other interfaces for data access and manipulation\nDocumentation & Reporting\nDocument data processes, pipelines, and architectures\nGenerate reports and visualisations to communicate data insights and pipeline performance\nTechnical Support\nProvide technical support for data-related issues and troubleshoot problems as they arise\nAssist in the implementation of data governance and compliance policies\nContinuous Improvement\nStay up to date with emerging trends and technologies in data engineering\nParticipate in code reviews and contribute to the continuous improvement of data engineering practices\nWhat success looks like in the role\nHelpdesk tickets are triaged promptly and accurately, and escalated to the right level without unnecessary back-and-forth\nData pipelines run reliably, with data quality issues caught through testing before they reach clients\nData is consolidated cleanly from multiple sources, giving analysts and scientists a dependable foundation to build on\nProcesses, pipelines, and architectures are well documented, so knowledge is shared rather than held in one head\nYou grow steadily in confidence across the stack, taking on more complex investigations, bug fixes, and change controls over time\nQualifications & Technical Skills\nProficiency in SQL and experience with relational databases (e.g. MySQL, PostgreSQL)\nExperience with data pipeline and workflow management tools (e.g. Apache Airflow)\nFamiliarity with programming languages such as Python, Java, or Scala\nBasic understanding of cloud platforms and services (e.g. AWS, Azure, Google Cloud)\nKnowledge of big data technologies (e.g. Spark) is a plus\nA bachelor's degree in Computer Science, Engineering, Information Technology, or a related field\nRelevant certifications in data engineering or cloud platforms (e.g. AWS, Azure, Google Cloud, Databricks) are a 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