SteadyIQ seeks a sharp, highly motivated data engineer who is excited to play a significant role in our product and engineering evolution. We seek product-focused, results-oriented engineers who thrive in a collaborative, team-focused culture. You will work closely with data scientists, backend engineers, DevOps, QA engineers, analysts, product managers, business stakeholders, and your team to help define, implement, and ship significant increments to the data infrastructure that powers SteadyIQ’s mission-driven products.
As a Senior Data Engineer, you will:
- Design, develop, and deliver high-quality, performant, scalable, well-tested, documented, and sensible solutions suitable to high-impact technical challenges.
- Administer, maintain, and improve SteadyIQ’s data infrastructure and data processing pipelines, including ETL jobs, events processing, and job monitoring and alerting.
- Engage in all agile software development lifecycle phases in a cross-functional team setting.
- Partner with Product, Engineering, DevOps, Data Science, and senior leadership to define and refine our data architecture and technology choices.
- Collaborate with data scientists and analysts to prepare complex data sets that can be used to solve difficult problems.
- Help define, implement, and reinforce data engineering best practices and processes.
- Safeguard SteadyIQ’s data by implementing/maintaining masking, access control, and protection policies.
Skills & Requirements:
- Significant data engineering and software development experience (5+ years).
- Experience developing scalable production software in Python.
- Familiarity with the AWS data ecosystem, including Step Functions, Lambda, CloudWatch, SQS, EventBridge, API Gateway, and SAM.
- Experience with partitioning strategies, indexing, and caching techniques for performance tuning.
- Proficiency in ingesting, processing, and transforming large-scale data in the cloud, ideally with spark-hosted Databricks and DLT pipelines.
- Expertise in optimizing queries and ETL processes for complex datasets.
- Experience automating ETL pipelines using DAGs with tools such as Airflow, Prefect, and Databricks Jobs.
- Demonstrated proficiency with SQL and NoSQL databases, plus data warehousing concepts.
- Ability to set up robust monitoring and alerting for data pipelines and implement data security frameworks like HIPAA or GDPR.
- Experience with encryption, data masking, role-based access control (RBAC), and developing CI/CD processes using Jenkins and GitHub actions.
- Ability to thrive in a fast-paced and dynamic environment and work well in teams of all sizes with representatives from diverse technical backgrounds.
Preferred:
- Experience with cost optimization in cloud environments, particularly in storage and compute efficiency (e.g., leveraging AWS Glue, S3 storage classes, or Lambda for serverless functions).
- Experience building or maintaining a data science development environment such as Databricks, including deployment and monitoring using tools like MLflow.
- Familiarity with infrastructure tools like Terraform, CloudFormation, OpenTofu, etc.
Educational Requirements:
- Bachelor’s or Master’s degree in Computer Science or equivalent experience.
Company Benefits:
- Health, Dental, and Vision Insurance;
- Paid parental leave;
- Vacation / Paid Time Off;
- 401k / Retirement plan;
- Learning stipend;
- Flextime / Remote Work;
- Stock options.
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