MathCo
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Python
Amazon Web Services (AWS)- 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)
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