Indicina offers technology solutions to empower businesses to offer credit to customers faster, more securely and at scale. Indicina is not a lender and does not offer loans to customers.
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We are looking for a savvy Senior Big Data Engineer to join our growing team of data science experts.
You will be responsible for creating, expanding, and optimizing data sets and data pipelines, as well as optimizing data collection and workflows used by our cross-functional Data Science team.
As a data engineer, you will partner with data scientists and product teams, to build full-stack data science solutions using data from some of the world’s largest cloud apps.
Job Duties and Responsibilities
Optimizing ETL processes: Assemble large, complex data sets to undertake a wide range analyses that answer a variety of questions
Data architecture: Design data schema and partner with engineering teams to implement high-perf views, including cloud-based workflows in R and Python
Work closely with Data Scientists to optimize and re-engineer model code to be modular, efficient and scalable, and to deploy models to production.
Configure, deploy, manage, and document data extraction, transformation, enrichment, and governance process in cloud data platforms, including AWS
Build visualizations to help derive meaningful insights from data.
Partner with data scientists, PMs, engineers and business stakeholders to understand business and technical requirements, plan and execute projects, and communicate status, risks and issues.
Perform root cause analysis of system and data issues and develop solutions as required. Design, develop, and maintain data pipelines and backend services for real-time decisioning, reporting, data collecting, and related functions
Manage CI/CD pipelines for developed services
Product analytics: Exploratory analysis and answer questions at various levels of product development
Communication: Communicate key insights and results with stakeholders and leadership. Interpret data/insights and use storytelling and data visualization to recommend product decisions.
Qualifications:
Bachelor degree in Computer Science (or related field) or 4 years of production experience
3-5+ years’ demonstrated experience with Big Data systems, ETL, data processing, and analytics tools.
5+ years architecting, building, and maintaining end-to-end data systems and supporting services
2+ years’ experience with relational databases as well as working familiarity with a variety of big data sources in Spark, Scala and/or other big data systems.
2+ years’ experience building and optimizing “big data” data workflows including data lake architecture, business intelligence pipelines and data visualization tools such as Power BI, Tableau or Metabase.
Experience with Amazon Web Services (AWS) such as Athena, Redshift, Glue, and others.
Monitor, validate, and drive continuous improvement to methods, and propose enhancements to data sources that improve usability and results.
Demonstrated proficiency in writing complex highly optimized queries across diverse data sets, and implementing data science applications in R and Python using cloud-based tools and notebooks (Jupyter, DataBricks, etc.).
Experience working with large datasets (terabyte scale and growing) and tooling
Production experience with building, maintaining, improving big data processing pipelines
Production experience with stream and batch data processing, data distribution optimization, and data monitoring
Experience maintaining a large software system and writing a test suite.
Experience with Continuous Integration, Version Control such as git.
Deep understanding of data structures and common methods in data transformation.
Excellent written and verbal communication skills
Preferred
Exposure to ML/AI techniques
Experience working with large data sets in SQL/Spark/Hadoop/Data Lake or similar
Experience with data ingestion and transforming data using tools such as Logstash, Fluentd, FluentBit or similar.
Experience with data warehousing with tools such as AWS Redshift, Google BigQuery, etc
Experience incorporating data processing and workflow management tools into data pipeline design
Experience developing on cloud platforms (i.e. AWS, Azure) in a continuous delivery environment
Ability to provide technical leadership to other level.
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