Data Architect

Key Skills

Aartificial intelligence, Apache Kafka, Azure, Azure COSMOS, Data Architecture, Data Governance, Data management, Data science techniques, Data Visualization, Database, Dataiku, DataOps, ETL/ELT technologies, Google BigQuery, Google Cloud Platform (GCP), Machine learning techniques, OLAP, PowerBI, PySpark, Python Programming, QlikView, Snowflake, SPARK Programming, Splunk, SQL, Tableau

Job Description

• Collaborate with stakeholders to develop a data strategy that meets enterprise needs and industry requirements.
• Create an inventory of the data necessary to build and implement a data architecture.
• Envision data pipelines and how data will flow through the data landscape.
• Evaluate current data management technologies and what additional tools are needed.
• Determine upgrades and improvements to current data architectures.
• Design, document, build and implement database architectures and applications. Should have hands-on experience in building high scale OLAP systems.
• Build data models for database structures, analytics, and use cases.
• Develop and enforce database development standards with solid DB/ Query optimizations capabilities.
• Integrate new systems and functions like security, performance, scalability, governance, reliability, and data recovery.
• Research new opportunities and create methods to acquire data.
• Develop measures that ensure data accuracy, integrity, and accessibility.
• Continually monitor, refine, and report data management system performance.

Required Qualifications and Skillset:

• Extensive knowledge of Azure, GCP clouds, and DataOps Data Eco-System (super strong in one of the two clouds and satisfactory in the other one)
• Hands-on expertise in systems like Snowflake, Synapse, SQL DW, BigQuery, and Cosmos DB. (Expertise in any 3 is a must)
• Azure Data Factory, Dataiku, Fivetran, Google Cloud Dataflow (Any 2)
• Hands-on experience in working with services/technologies like - Apache Airflow, Cloud Composer, Oozie, Azure Data Factory, and Cloud Data Fusion (Expertise in any 2 is required)
• Well-versed with Data services, integration, ingestion, ELT/ETL, Data Governance, Security, and Meta-driven Development.
• Expertise in RDBMS (relational database management system) – writing complex SQL logic, DB/Query optimization, Data Modelling, and managing high data volume for mission-critical applications.
• Strong grip on programming using Python and PySpark.
• Clear understanding of data best practices prevailing in the industry.
• Preference to candidates having Azure or GCP architect certification. (Either of the two would suffice)
• Strong networking and data security experience.

Awareness of the Following:

• Application development understanding (Full Stack)
• Experience on open-source tools like Kafka, Spark, Splunk, Superset, etc.
• Good understanding of Analytics Platform Landscape that includes AI/ML
• Experience in any Data Visualization tool like PowerBI / Tableau / Qlik /QuickSight etc.

About Us

Gramener is a design-led data science company. We build custom Data & AI solutions that help solve complex business problems with actionable insights and compelling data stories. We partner with enterprise data and digital transformation teams to improve the data-driven decision-making culture across the organization. Our open standard low-code platform, Gramex, rapidly builds engaging Data & AI solutions across multiple business verticals and use cases. Our solutions and technology have been recognized by analysts such as Gartner and Forrester and have won several awards.

We Offer You:

• a chance to try new things & take risks.
• meaningful problems you'll be proud to solve.
• people you will be comfortable working with.
• transparent and innovative work environment.