Company Description

Tamr is the enterprise data mastering company trusted by large enterprises like Blackstone, the US Air Force, Toyota, and GSK. The company’s patented software platform uses machine learning supplemented with human feedback to master and prepare data across myriad silos to deliver previously unavailable business-changing insights. With a co-founding team led by Andy Palmer (founding CEO of Vertica) and Mike Stonebraker (Turing Award winner) and backed by top-tier investors such as NEA and GV, Tamr is transforming how companies get value from their data.

Tamr’s cloud-native data mastering solutions use machine learning to do the heavy lifting to consolidate, cleanse, and categorize data, with intuitive human feedback workflows to bridge the gap between data and business outcomes. Built to scale with cloud-native capabilities for the leading cloud providers, machine learning to accelerate time-to-insights and data initiatives, and APIs for effective integration with new and existing pipelines, Tamr is the foundation for modern DataOps companies.

We are looking for a data platform engineer to be a key contributor in our enrichment and data products team. You will be responsible for building the data platform that our data scientists and data engineers use to build data products. You will develop frameworks and processes that guide our data team and you’ll help to build, maintain, and monitor automated data pipelines. You’ll work in a collaborative and entrepreneurial team of data scientists, data engineers, software engineers, product managers, and business leads. You will be a senior individual contributor in our team and will help to guide the technical direction of the data engineering performed by our team.

Responsibilities:

• Establish the architecture for curating data from the web and 3rd party datasets into a handful of data products for key business entities (e.g., companies, people, parts)
• Evaluate vendors and develop the tooling required to execute on the defined architecture
• Build automated data pipelines in the cloud
• Establish a high standard of data quality and freshness, consistent with Tamr’s overall positioning in the market
• Collaborate with data scientists to incorporate machine learning into data pipelines 
• Collaborate with software engineers on developing applications to deliver data

Challenges that make this job interesting:

• The problem we’re solving is hard - enterprise data is messy and there is a lot of it. It’s our job to derive value from this data in a flexible and scalable way
• We’re working at the cutting edge - we’re responsible for the innovation, experimentation, and development that goes into building new products that our customers find useful
• Our team is interdisciplinary - we work at the intersection of data science, data engineering, and software engineering. We’re looking for people who love to learn and are happy to take on new challenges

Qualifications:

• 7+ years of industry experience
• You’ve built big data pipelines in the cloud (GCP, AWS, or Azure)
• You’ve a deep understanding ETL theory and technologies 
• You’re a polyglot programmer, with experience using technologies such as Python, Java, SQL, Git & GitHub, and Jenkins
• You’re experienced with big data technologies such as Hadoop, Dataproc/Databricks/Spark, BigQuery
• You’ve used a wide variety of data repositories: data warehouses, data lakes etc. 
• You’ve automated big data pipelines with technologies like Airflow
• You’ve built simple containerized applications 
• You’ve used infrastructure-as-code tools like Terraform

Other Preferred Qualifications / Nice to have:

• You have an interest in machine learning and have trained and tested a few machine learning models 

Additional Information

This position is available in Cambridge, MA, hybrid/flexible schedule. Tamr does sponsor employees requiring a visa.

Tamr provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, marital status, amnesty, or status as a covered veteran in accordance with applicable federal, state and local laws.