{"id":167804,"url":"https://alion.io/job/novanta-senior-data-engineer","title":"Senior Data Engineer","company":{"id":58917,"name":"Novanta","domain":"novanta.com","url":"https://alion.io/company/novanta","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":83,"open_postings":15,"ghost_share":0,"stale_share":0.667,"repost_share":0,"time_to_fill_p50_days":40,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US","CN","IN","DE","JP","FR","GB","AR","AU","AT","BE","BR","BG","CA","HR","CZ","DK","FI","HU","ID","IE","IT","MY","MX","NL","NZ","NO","PK","PH","PL","RO","KR","ES","SE","CH","TW","TH","VN","AS","AG"],"hiring_countries_total":126,"salary":{"min":101100,"max":161800,"currency":"USD","period":"year","gross":null,"usd_annual":161800},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure DevOps","optional":false},{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"Embeddings","optional":false},{"name":"Fivetran","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Informatica","optional":false},{"name":"LLM","optional":false},{"name":"Matillion","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Tool Use","optional":false},{"name":"Azure","optional":true}],"status":"live","first_seen_at":"2026-07-30T00:00:00Z","employer_posted_date":"2026-07-30","last_verified_at":"2026-10-16T21:34:03Z","board_verified":true,"closed_at":null,"days_open":79,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":79},"description":"Build a career powered by innovations that matter! At Novanta, our innovations power technology products that are transforming healthcare and advanced manufacturing-improving productivity, enhancing people’s lives and redefining what’s possible. We create for our global customers engineered components and sub-systems that deliver extreme precision and performance for a range of mission-critical applications-from minimally invasive surgery to robotics to 3D metal printing.\nNovanta is one global team with over 26 offices located in The Americas, Europe and Asia-Pacific. Looking for a great place to work? You have found it with a culture that embraces teamwork, collaboration and empowerment. Come explore Novanta.\nSummary\nThe Senior Data Engineer owns Novanta’s governed transformation layer end-to-end. As a senior individual contributor, you will design and build the Snowflake transformation layer (raw → staging → conformed → marts), migrate legacy SQL views and user-maintained dimensions into versioned, tested dbt models with full lineage, and define the conformed dimensions and operational facts that unify our multi-ERP environment across the AET and Medical Solutions segments. You will set the modeling, testing, naming, documentation, and CI/CD standards that the future Data Engineer and MDM hires on this team will inherit. This role is hands-on; you should expect to spend most of your time in dbt, SQL, and Python rather than in meetings.\nAbout the team\nNovanta’s Corporate & Shared Services team serves as a strategic partner to the company’s global business units, providing the expertise, systems, and infrastructure that enable growth, operational excellence, and innovation. Comprised of professionals across Finance, Accounting, Human Resources, Information Technology, Legal, Compliance, Corporate Development, and Corporate Marketing, the team collaborates closely with leaders across the organization to drive business performance, support strategic initiatives, enhance employee and customer experiences, and ensure the scalability of Novanta’s global operations\nTransformation Layer Design & Build\nDesign and build the transformation layer in Snowflake (raw → staging → conformed → marts) as the governed foundation for all downstream reporting and AI/ML use cases.\n\nMigrate existing SQL views to dbt-style versioned, tested models with full lineage.\n\nConformed Dimensions & Dimensional Modeling\nDefine conformed dimensions and operational facts across Novanta’s multi-ERP environment using Kimball dimensional modeling principles - star schemas, surrogate keys, fact grain decisions, and SCD Type 1 and Type 2 patterns where appropriate.\n\nSet modeling, testing, naming, documentation, and CI/CD standards for the team, including Git workflows, code review practices, dbt project structure, model contracts, and release management.\n\nBI Migration & Governance\nPartner with report owners and stakeholders to validate semantic equivalence and minimize disruption during cutover from past datasets.\n\nDefine and own the semantic layer where applicable (Snowflake Semantic layer, dbt Semantic Layer, Power BI semantic models) so that metric definitions are consistent across BI, AI, and embedded use cases.\n\nImplement data quality testing, observability, and lineage (dbt tests, Elementary, or equivalent), and partner with IT Audit on SOX-relevant controls for financial data flows.\n\nAI / ML & Agentic Readiness\nDesign models with downstream AI/ML and agent consumption in mind: documented model contracts, stable surrogate keys, semantically clear column names, and metadata sufficient for LLM-based retrieval and tool use.\n\nPartner with the AI/analytics team on feature-table patterns, golden-record exposure, and the data foundations required to support emerging agentic applications across Finance, Operations, and Commercial.\n\nMentorship & Team Leadership\nBachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related quantitative field; equivalent professional experience considered.\n\n7+ years in data engineering or analytics engineering, including 4+ years building production workloads on a cloud data warehouse (Snowflake strongly preferred; BigQuery, Databricks, or Redshift considered with equivalent depth).\n\nExpert SQL-window functions, performance tuning, and incremental load patterns at scale.\n\n3+ years of dbt in production (or an equivalent transformation framework), including macros, generic and singular tests, snapshots, incremental strategies, exposures, and documentation. You have designed a dbt project from scratch and maintained it in production.\n\nHands-on experience with Kimball dimensional modeling - star schemas, conformed dimensions, fact grain decisions, surrogate keys, and SCD Type 1 and Type 2 patterns.\n\nProduction experience with at least one ingestion tool (Fivetran, Airbyte, Informatica, or Matillion) and one orchestrator (dbt Cloud, Airflow, Dagster, or Azure Data Factory).\n\nHands-on Git and CI/CD for data (GitHub Actions, Azure DevOps Pipelines, or GitLab CI), including code review and release management practices.\n\nProficient in Python for data tooling - dbt macros, automation scripts, data quality frameworks, and lightweight API integration. You are not required to be a software engineer; [redacted]\n\nDirect experience modeling data sourced from at least one major enterprise ERP (Oracle, SAP S/4HANA, SAP ECC, Microsoft Dynamics, or comparable).\n\nStrong written communication-able to author standards, runbooks, and documentation that non-engineers can follow.\n\nSelf-starter who thrives in a fast-paced, multi-ERP environment with minimal supervision and competing stakeholder priorities.\n\nExperience supporting AI/ML, RAG, or agentic workloads from a warehouse layer (feature tables, embeddings, semantic metadata).\n\nPreferred Qualifications\nTable-level experience with SAP ECC and/or S/4HANA finance and logistics tables (e.g., BSEG, BKPF, MARA, EKKO, VBAK).\n\nHands-on experience with Power BI semantic models, DAX, and certified dataset patterns.\n\nPrior data engineering or analytics experience in manufacturing, medical devices, or another regulated industry.\n\nFamiliarity with SOX ITGC and IT-audit controls for financial data lineage at a publicly traded company.\n\nExposure to MDM tooling and processes (Reltio, Informatica MDM, Stibo, or comparable).\n\nCompensation\nThe salary for this role will range from 101,100.00 - 161,800.00 USD annual based on full-time employment. Salary offers are based on a wide range of factors including but not limited to location, relevant skills, training, experience, education, etc.\n\nCertain roles may be eligible for performance-based incentive compensation and/or long-term incentives. Incentives could be discretionary or non-discretionary depending on the plan.\n\nNovanta supports all aspects of your life’s needs. This position provides a full range of medical, financial, and other benefits to make your quality of life better\n\nTravel Requirements\nOccasional travel is required to Novanta global locations.\nPhysical Requirements\nMobility to work in a standard office setting and to use standard office equipment, including a computer. Ability to use vision to read a computer screen and read printed materials.\nNovanta is proud to be an equal employment opportunity and affirmative action workplace. 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