Principal Data Analyst
India, Bengaluru, Mantri
Analog Devices
Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $12 billion in FY22 and approximately 25,000 people globally working alongside 125,000 global customers, ADI ensures today’s innovators stay Ahead of What’s Possible.
- Gather business and functional requirements and translate into robust scalable, solutions aligned to the Data Architecture guidelines
- Hands on development work on all aspects of data analysis, data provisioning, Data modeling, performance tuning and optimization
- Design, implement and operate large scale, high volume, high performance data structures for Analytics
- working knowledge of Spark, Presto, Hive, SQL and Python
- Experience building on atleast one of the cloud technologies (AWS, GCP, Azure, Snowflake, Databricks)
- Lead the execution of large scale, complex data and analytics efforts, as well as mentor complex projects using a wide breadth of data science and analytical techniques.
- Develop in-depth subject matter-expertise in understanding key strategic data sources and data lineage within the organization.
- Scope key business challenges, identify the right data and provide direction to data analysts, data analytics product managers, data scientists, data engineers and business stakeholders.
- Communicate the value of data clearly by articulating both the high-level concept and detailed user stories for feature enhancements and convince teams to adopt data-driven improvements.
- Drive innovation by creating new frameworks, standards, prototypes, and automation projects in relevant areas.
Qualifications
- 10+ years of experience in software engineering, including 7+ years working with data engineering technologies.
- 5+ years leading technical teams and managing complex data engineering projects and pipelines.
- Degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field.
- Broad expertise and experience with distributed systems, streaming systems, and data engineering tools, such as Kubernetes, Kafka, Airflow, Dagster, etc.
- Deep knowledge of Python, SQL, database design, and master data strategies.
- Experience defining, architecting, and rolling out data products, including ownership of data products through their entire lifecycle.
- Experience mentoring and leading junior technical staff, incorporating modern software development tools and practices.
- A confident peer influencer with strong communication skills who quickly establishes credibility and is capable of leading cross-functional technical teams
For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export licensing approval from the U.S. Department of Commerce - Bureau of Industry and Security and/or the U.S. Department of State - Directorate of Defense Trade Controls. As such, applicants for this position – except US Citizens, US Permanent Residents, and protected individuals as defined by 8 U.S.C. 1324b(a)(3) – may have to go through an export licensing review process.
Analog Devices is an equal opportunity employer. We foster a culture where everyone has an opportunity to succeed regardless of their race, color, religion, age, ancestry, national origin, social or ethnic origin, sex, sexual orientation, gender, gender identity, gender expression, marital status, pregnancy, parental status, disability, medical condition, genetic information, military or veteran status, union membership, and political affiliation, or any other legally protected group.
Job Req Type: ExperiencedRequired Travel: Yes, 10% of the time
Shift Type: 1st Shift/Days
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Airflow Architecture AWS Azure Computer Science Dagster Data analysis Data Analytics Databricks Distributed Systems Engineering GCP Kafka Kubernetes Pipelines Python Security Snowflake Spark SQL Streaming
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