Senior Data Engineer

Novanta Inc.

28d•$101,100 - $161,800•Hybrid

About The Position

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. Novanta 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. Summary The 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. About the team Novanta’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

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related quantitative field; equivalent professional experience considered.
7+ 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).
Expert SQL—window functions, performance tuning, and incremental load patterns at scale.
3+ 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.
Hands-on experience with Kimball dimensional modeling — star schemas, conformed dimensions, fact grain decisions, surrogate keys, and SCD Type 1 and Type 2 patterns.
Production experience with at least one ingestion tool (Fivetran, Airbyte, Informatica, or Matillion) and one orchestrator (dbt Cloud, Airflow, Dagster, or Azure Data Factory).
Hands-on Git and CI/CD for data (GitHub Actions, Azure DevOps Pipelines, or GitLab CI), including code review and release management practices.
Proficient 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; you are required to write maintainable Python that solves data problems.
Direct experience modeling data sourced from at least one major enterprise ERP (Oracle, SAP S/4HANA, SAP ECC, Microsoft Dynamics, or comparable).
Strong written communication—able to author standards, runbooks, and documentation that non-engineers can follow.
Self-starter who thrives in a fast-paced, multi-ERP environment with minimal supervision and competing stakeholder priorities.

Nice To Haves

Experience supporting AI/ML, RAG, or agentic workloads from a warehouse layer (feature tables, embeddings, semantic metadata).
Table-level experience with SAP ECC and/or S/4HANA finance and logistics tables (e.g., BSEG, BKPF, MARA, EKKO, VBAK).
Hands-on experience with Power BI semantic models, DAX, and certified dataset patterns.
Prior data engineering or analytics experience in manufacturing, medical devices, or another regulated industry.
Familiarity with SOX ITGC and IT-audit controls for financial data lineage at a publicly traded company.
Exposure to MDM tooling and processes (Reltio, Informatica MDM, Stibo, or comparable).

Responsibilities

Design and build the transformation layer in Snowflake (raw → staging → conformed → marts) as the governed foundation for all downstream reporting and AI/ML use cases.
Migrate existing SQL views to dbt-style versioned, tested models with full lineage.
Define 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.
Set 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.
Partner with report owners and stakeholders to validate semantic equivalence and minimize disruption during cutover from past datasets.
Define 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.
Implement data quality testing, observability, and lineage (dbt tests, Elementary, or equivalent), and partner with IT Audit on SOX-relevant controls for financial data flows.
Design 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.
Partner 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.
Mentor and lead the team.