Key skills
AirflowAWSAzureCloudGoogle Cloud PlatformHadoopPythonSparkSQLAIMLData EngineeringAnalyticsSnowflakeDatabricksGCPGoogle CloudPerformance OptimizationCI/CD
About this role
Role Overview
- Designing, building and owning next-generation data product engineering patterns on modern cloud platforms including Snowflake and Databricks.
- Providing technical guidance and mentorship to other data engineers, promoting consistent engineering practices and high‑quality solutions.
- Developing reusable engineering assets such as frameworks, build kits, CI/CD templates, and performance optimization approaches.
- Partnering with Enablement and Execution teams to operationalize and scale data engineering patterns across delivery teams, serving as a technical point of reference for adoption and implementation.
- Evaluating, testing, and experimenting with emerging data and AI tools, platforms, and services.
- Participate in technical proofs of concept, comparing alternative solutions, and making data-driven recommendations for platform and tool rationalization.
- Documenting project outcomes, transition plans, adoption guides, and solution usage scripts to support enterprise rollout.
- Supporting platform modernization efforts through hands-on development, tuning, and optimization.
- Collaborating with data product owners, architects, and platform teams to align engineering solutions with enterprise data strategy.
Requirements
- Bachelor’s Degree in a quantitative field such as computer science, data science, mathematics, or statistics.
- 6 to 8+ years of statistical and/or analytical experience.
- Typically, 8+ years of experience in data engineering, analytics engineering, or platform engineering roles.
- Deep understanding of financial institution/Banking concepts.
- Strong understanding of modern data engineering concepts, including batch and streaming data processing, data modeling, and data product design.
- Experience building scalable data solutions on cloud-based data platforms.
- Familiarity with enterprise data ecosystems and shared platform models.
- Ability to assess tradeoffs across tools, architectures, and implementation approaches.
- Strong analytical and problem-solving skills with a focus on root cause analysis and optimization.
- Proficiency with big data technologies (Spark, Airflow, Hadoop, Hive).
- Hands-on experience with Snowflake and Databricks, including performance tuning.
- Proficiency in SQL and Python, with experience building production-grade data pipelines.
- Experience with CI/CD pipelines and infrastructure-as-code patterns for data platforms.
- Familiarity with orchestration and workflow management tools.
- Experience developing reusable libraries, templates, or internal frameworks.
- Exposure to cloud platforms such as Azure, AWS, or GCP and cloud-native data services.
- Understanding data quality, observability, and monitoring practices.
- Familiarity with AI and ML tooling as it relates to data engineering and platform enablement is a plus.
Tech Stack
- Airflow
- AWS
- Azure
- Cloud
- Google Cloud Platform
- Hadoop
- Python
- Spark
- SQL
Benefits
- Healthcare (medical, dental, vision)
- Basic term and optional term life insurance
- Short-term and long-term disability
- Pregnancy disability and parental leave
- 401(k) and employer-funded retirement plan
- Paid vacation (from two to five weeks depending on salary grade and tenure)
- Up to 11 paid holiday opportunities
- Adoption assistance
- Sick and Safe Leave accruals of one hour for every 30 worked, up to 80 hours per calendar year unless otherwise provided by law