Data Engineer/Sr. Data Engineer
- Houston, TX, USA
- Employees can work remotely
Our focus is to help high-growth companies leverage AI to solve today’s hardest challenges. We are driven to make the world a better place by providing consulting services and building applications to improve care, enhance discoveries, and enable data-driven decisions.
Our team is composed of creative and results-focused individuals who excel at solving real-world problems. Our diverse backgrounds bring technology and expertise from various disciplines including neuroscience, physics, engineering, computational biology, genomics, mathematics, and computer science.
Our culture is vibrant, connected, rooted in our core values statement that: Our work matters. Our clients are partners. Our work is our reputation. We own our choices. We are always learning. We support and challenge each other.
This role is eligible for flexible hours and remote work.
This role is ideal for a results-focused individual who excels at solving problems at the cutting edge of machine learning engineering and data engineering with a good dose of DevOps/DataOps/MLOps experience. This is a key role in developing further expertise in ML, Data, and DevOps Engineering across Mercury Data Science. Candidates must have a passion for leading and evolving the Data/ML Engineering, and MLOps space with thought leadership and innovation.
Responsibilities also include consulting on customer engagements, providing technical assessments, reviewing the architecture and strategy of Data & AI/ML solutions, and ensuring that they align with industry best/emerging practices to most closely align with customer goals and business objectives.
- Experience with productionized AI/ML systems - Data Science solutions in production is our true measure of success
- Broad cloud experience - supporting clients across all cloud providers including organizations with hybrid on-prem
- Strong understanding of data stores and their tradeoffs - we utilize a broad range of databases (Relation, Key-Value, Document, Object, Graph)
- Strong understanding of Big Data tooling with the Cloud - we deal with all different data sizes, small to large - efficient and timely processing is critical to our success
- Strong understanding of DevOps and code best practices - as a Data Science company we believe strongly in automation for everything including our code and deployment
- Good understanding of MLOps and ML best practices - as a leader in Data Science we stay current on the latest developments and believe MLOps is the future
- Strong project/product management with previous experience guiding agile development practices
Tools We Love
- DevOps: Terraform, Docker, Kuberentes, git, Jenkins, and related toolsets
- ML Workflow Tooling: Sagemaker, MLFlow, Kubeflow, DVC, Argo+Polyaxon+Seldon, Argo
- Data Processing: dbt, python+pandas, AWS Glue (Jobs), Spark/pySpark, Fivetran, Matillion, Dask
- Data Warehousing/Lakes: Redshift, Snowflake, BigQuery, Synapse, Athena, S3/gcs/ADLS
Nice to have
- Cloud platform certifications (AWS Associate Architect, Associate Developer, or higher)
- Managed a cross-functional team in the delivery of Data-drive and AI-driven applications to a customer
- Delivered an end-to-end data workflow in a production environment
- Understanding of Information Security best practices for applications and company requirements
- Exposure to PII and HIPAA requirements and compliance for data storage and access
- Ability to support and provide guidance to the organization on all things technical, including more typical IT challenges
- Ability to design cloud and on-premises solutions given client requirements, gaps and, in-depth systems analysis
- Junior Engineer - 0-2 years experience/relative skills
- Engineer - 1-4 years experience/relative skills
- Senior Engineer - 3+ years experience/relative skills