{"id":1129207,"url":"https://alion.io/job/abbott-laboratories-staff-data-engineer","title":"Staff Data Engineer","company":{"id":2710,"name":"Abbott Laboratories","domain":"abbott.com","url":"https://alion.io/company/abbott","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":100,"open_postings":84,"ghost_share":0,"stale_share":0,"repost_share":0.012,"time_to_fill_p50_days":15,"computed_at":"2026-09-27T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Madison, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":99300,"max":198700,"currency":"USD","period":"year","gross":null,"usd_annual":198700},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"GCP","optional":false},{"name":"HIPAA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Apache Kafka","optional":true},{"name":"Delta Lake","optional":true},{"name":"Rest API","optional":true},{"name":"Tableau","optional":true}],"status":"live","first_seen_at":"2026-09-22T00:00:00Z","employer_posted_date":"2026-09-22","last_verified_at":"2026-09-27T23:01:34Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 122,000 colleagues serve people in more than 160 countries.JOB DESCRIPTION:\nThe Staff Data Engineer is a senior, hands-on technical contributor within an Enterprise Data domain, owning technical design and the most complex implementation work for assigned data products, capabilities, and integrations. The role establishes, communicates, and evolves the technical approach for the work it leads and is accountable for the quality, durability, and supportability of the solutions it shapes.\nStaff Data Engineers work directly with business stakeholders to understand needs and shape technical solutions, engaging at a level appropriate to the work they lead. The role is expected to be fluent in both technical execution and business context, translating between them without losing precision in either.\nThe Staff Data Engineer sets technical direction for assigned capabilities and initiatives within the domain, applies enterprise standards and platform patterns, engages Principal Engineers where cross-domain considerations apply, and multiplies the effectiveness of the domain team through design leadership, code review, and mentoring.\nThis role is based in Madison, WI.\nEssential Duties\nInclude, but are not limited to, the following:\nTechnical design and solutioning\nOwn technical design and hands-on delivery for the most complex or highest-risk work across assigned data products, capabilities, and initiatives, including data models, pipeline architecture, integration patterns, and platform usage decisions.\nProduce design documentation that allows others to understand, review, and build on technical decisions, and drive design review with domain and cross-domain peers.\nBuild and evolve assigned domain data products so they are documented, discoverable, governed, observable, and supported throughout their lifecycle for reuse across analytics, semantic layers, machine learning, and AI applications.\nDefine and uphold data contracts for the products the domain publishes, including schemas, freshness and availability expectations, breaking-change policy, and producer and consumer responsibilities.\nDesign data products for AI and machine learning consumption with reproducibility, lineage, timeliness, and defined data quality controls appropriate to the use case.\nDiagnose and resolve complex production and data quality issues, and drive root cause resolution rather than recurring remediation.\nUse approved AI-assisted development tooling where appropriate to accelerate engineering work, validating output against correctness, security, privacy, and quality standards.\nOwn the technical cost efficiency of assigned data products, including compute and warehouse sizing, job and query optimization, serverless and storage tradeoffs, and cost-to-serve implications.\nEvaluate and recommend tools, patterns, and platform capabilities within enterprise standards, raising cases where an exception may be warranted.\nStakeholder engagement\nWork directly with business stakeholders to understand needs, clarify requirements, and shape technical solutions.\nCommunicate technical concepts, tradeoffs, constraints, and delivery implications clearly to non-technical audiences.\nParticipate in technical working sessions with partner organizations such as Software Engineering, IT Applications, and Enterprise Architecture on integration and design questions affecting the domain.\nSurface scope, priority, resourcing, and technical-debt implications of technical decisions for leadership review rather than resolving them independently.\nTechnical leadership and multiplication\nServe as a technical lead for assigned domain capabilities and initiatives, guiding engineers through design and implementation without formal authority.\nProvide code and design review that raises quality and consistency across the domain team.\nMentor engineers and support their technical growth, including engineers transitioning into data engineering from adjacent disciplines.\nEngage Principal Engineers on cross-domain architecture patterns and enterprise standards, and contribute domain perspective to initiatives that extend beyond the domain.\nLead adoption and continuous improvement of domain testing, observability, CI/CD, and delivery practices within enterprise standards.\nOperational excellence and compliance\nEnsure delivered solutions meet requirements for quality, reliability, scalability, performance, observability, security, privacy, access, and lifecycle management.\nDesign and implement appropriate handling of protected health information and other sensitive data, including access control, masking and de-identification, lineage, retention, and audit support consistent with HIPAA, CLIA, and enterprise privacy standards.\nServe as domain subject matter expert for significant production incidents affecting domain systems.\nUphold company mission and values through accountability, innovation, integrity, quality, and teamwork.\nSupport and comply with the company's Quality Management System policies and procedures.\nMaintain regular and reliable attendance.\nAbility to act with an inclusion mindset and model these behaviors for the organization.\nMinimum Qualifications\nBachelor's degree in Data Science, Computer Science, Information Systems, Mathematics, or Engineering.\n8+ years of progressively responsible experience in data engineering or a closely related discipline.\nDemonstrated experience owning technical design and hands-on delivery for complex data products, pipelines, or platform capabilities from design through production support.\nAdvanced SQL and Python, with experience applying relational, dimensional, and semi-structured data modeling approaches.\nExperience designing and operating scalable, reliable distributed data systems on a modern data platform such as Databricks, including Spark, and using cloud infrastructure services such as AWS, Azure, or Google Cloud Platform.\nExperience with version control, automated testing, and CI/CD practices, and with orchestration and transformation tooling such as Databricks Workflows, Delta Live Tables, Airflow, or dbt.\nExperience delivering governed data using catalog, lineage, access control, and data quality capabilities such as Unity Catalog.\nDemonstrated ability to work directly with business stakeholders to translate needs into technical solutions, mentor engineers, and raise technical quality across a team.\nDemonstrated ability to perform the essential duties of the position with or without accommodation.\nPreferred Qualifications\nExperience with streaming and event-driven architectures such as Kafka.\nExperience in a regulated environment such as HIPAA, CLIA, SOX, FDA, ISO 13485, or IEC 62304, ideally in life sciences, diagnostics, or clinical laboratory data handling protected health information.\nExperience with semantic layers, metric definitions, data products, or feature pipelines supporting analytics, machine learning, and AI use cases in production.\nExperience with open data file and table formats such as Parquet, Avro, and Delta Lake.\nExperience with REST API development and integration patterns.\nFamiliarity with business intelligence concepts and semantic consumption patterns, including how data models and platform design affect performance in tools such as Tableau.\nThe base pay for this position is\n$99,300.00 - $198,700.00In specific locations, the pay range may vary from the range posted.\nJOB FAMILY:\nProduct DevelopmentDIVISION:\nONCO Cancer DiagnosticsLOCATION:\nUnited States > Madison : 1 Exact LaneADDITIONAL LOCATIONS:\nWORK SHIFT:\nStandardTRAVEL:\nYes, 5 % of the TimeMEDICAL SURVEILLANCE:\nNoSIGNIFICANT WORK ACTIVITIES:\nContinuous sitting for prolonged periods (more than 2 consecutive hours in an 8 hour day), Keyboard use (greater or equal to 50% of the workday)Abbott is an Equal Opportunity Employer of Minorities/Women/Individuals with Disabilities/Protected Veterans.EEO is the Law link - English: http://webstorage.abbott.com/common/External/EEO_English.pdfEEO is the Law link - Espanol: http://webstorage.abbott.com/common/External/EEO_Spanish.pdf","description_format":"text","description_chars":8368,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Prescription Drugs","Cardiovascular Devices","Diabetes Devices","Neurotech & Neuromodulation"],"lifecycle":[{"event":"open","at":"2026-09-23T00:18:53Z"}],"liveness":{"score":80,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.804,"p_room":1,"age_days":5,"expected_fill_days":15,"reasons":["conf:0","velocity","win:early","comp:brand"],"computed_at":"2026-09-27T05:45:00Z"},"pay":{"stated_usd_annual":198700,"is_top_pay":false},"html_url":"https://alion.io/job/abbott-laboratories-staff-data-engineer","json_url":"https://alion.io/job/abbott-laboratories-staff-data-engineer.json","meta":{"generated_at":"2026-09-28T02:43:08Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1466,"day_limit":5000,"remaining_today":3534,"minute_limit":60,"resets_at":"2026-09-29T00:00:00Z"}}}