MathCo

• Responsible for designing, storing, processing, and maintaining of large-scale data and related infrastructure.
• Can drive multiple projects both from operational and technical standpoint.
• Ideate and build PoV or PoC for new product that can help drive more business.
• Responsible for defining, designing, and implementing data engineering best practices, strategies, and solutions.
• Is an Architect who can guide the customers, team, and overall organization on tools, technologies, and best practices around data engineering.
• Lead architecture discussions, align with business needs, security, and best practices.
• Has strong conceptual understanding of Data Warehousing and ETL, Data Governance and Security, Cloud Computing, and Batch & Real Time data processing
• Has strong execution knowledge of Data Modeling, Databases in general (SQL and NoSQL), software development lifecycle and practices, unit testing, functional programming, etc.
• Understanding of Medallion architecture pattern
• Has worked on at least one cloud platform.
• Has worked as data architect and executed multiple end-end data engineering project.
• Has extensive knowledge of different data architecture designs and data modelling concepts.
• Manages conversation with the client stakeholders to understand the requirement and translate it into technical outcomes.

Required Tech Stack

• Strong proficiency in SQL
• Experience working on any of the three major cloud platforms i.e., AWS/Azure/GCP
• Working knowledge of an ETL and/or orchestration tools like IICS, Talend, Matillion, Airflow, Azure Data Factory, AWS Glue, GCP Composer, etc.
• Working knowledge of one or more OLTP databases (Postgres, MySQL, SQL Server, etc.)
• Working knowledge of one or more Data Warehouse like Snowflake, Redshift, Azure Synapse, Hive, Big Query, etc.
• Proficient in at least one programming language used in data engineering, such as Python (or Scala/Rust/Java)
• Has strong execution knowledge of Data Modeling (star schema, snowflake schema, fact vs dimension tables)
• Proficient in Spark and related applications like Databricks, GCP DataProc, AWS Glue, EMR, etc.
• Has worked on Kafka and real-time streaming.
• Has strong execution knowledge of data architecture design patterns (lambda vs kappa architecture, data harmonization, customer data platforms, etc.)
• Has worked on code and SQL query optimization.
• Strong knowledge of version control systems like Git to manage source code repositories and designing CI/CD pipelines for continuous delivery.
• Has worked on data and networking security (RBAC, secret management, key vaults, vnets, subnets, certificates)