AWS Data Engineer, Fractal
AI-driven data analytics platform
Open for applications
AI-driven data analytics platform
1001+ employees
Open for applications
1001+ employees
Company mission
To reshape business through science and advanced data tools and uncomplicate decision-making, so human intelligence can be freed from the drudgery of monotonous work and committed to the pursuit of imagination and creative rewards.
Role
Who you are
- 6 years’+ experience in the industry
- 3 or more years of hands-on experience on the AWS Services, especially AWS Glue
- Experience and knowledge of Big Data Architectures, on cloud and on premise
- Working experience with: AWS Athena and Glue Pyspark, EMR, DynamoDB, Redshift, Kinesis, Lambda, Apache Spark, Databricks on AWS, Snowflake on AWS
- Proficient in AWS Redshift, S3, Glue, Athena, DynamoDB
- AWS Certification: AWS Certified Solutions Architect and/or AWS Certified Data Analytics
- Capability to design production-grade dbt pipelines using reusable macros, incremental models, and SCD Type 2 implementations
- Deep knowledge of platform-specific tuning, such as clustering keys in Snowflake or dist/sort keys in Redshift and table transformation using dbt
- Proficiency in Python for building custom Airflow operators, reusable ingestion frameworks, and complex workflow automation
- Detailed understanding of AWS data share capabilities for sharing data across account and redshift instances
- Working experience with Agile Methodology, work across all phases of SDLC, and use Software Engineering principles to build scaled solutions
- Strong hands-on experience in AWS Glue, automation build-out, integration with other AWS services, using SPARK
- Good knowledge with Spark and Python
- Experience in building and delivering proofs-of-concept, in order to address specific business needs, using the most appropriate techniques, data sources and technologies
- Experience partnering with executive stakeholders as a trusted advisor as well as enabling technical implementers
- Hands-on administrating skill required with AWS Managed Kafka configuration for creating topics, consumer and producer control
- Need to review and decompose the existing Java application to be replaced with either a Mule service built by the Mule squad, or Lambda service in Python within the Operational Data squad. Ideally suited to someone who has worked on Java development in past
- Be an integral part of large-scale client business development and delivery engagements
What the job involves
- As a AWS Data Engineer in the insurance sector, If you are an extraordinary developer and who loves to push the boundaries to solve complex business problems using creative solutions, then we wish to talk with you
- As an Analytics Technology Engineer, you will work on the Technology team that helps deliver our Data Engineering offerings at large scale to our Fortune clients worldwide. The role is responsible for innovating, building, and maintaining technology services
- Consult, Design, build and operationalize large scale enterprise data solutions using one or more of AWS data and analytics services in combination with 3rd parties - Spark, EMR, DynamoDB, RedShift, Kinesis, Lambda, Glue, Snowflake and Databricks
- Analyse, re-architect and re-platform on-premises data stores / Databases to modern data platforms on AWS cloud using AWS or 3rd party services
- Design and build production data pipelines from ingestion to consumption within a big data architecture, using Java, Python, Scala
- Design and optimize data models on AWS Cloud using AWS data stores such as Redshift, DynamoDB, RDS, S3
- Design and implement data engineering, ingestion and curation functions on AWS cloud using AWS native or custom programming
- Perform detail assessments of current state data platforms and create an appropriate transition path to AWS cloud as part of customer consultation and business proposals
- Participate in client design workshops and provide trade-offs and recommendations towards building solutions
- Mentor other engineers in coding best practices and problem solving
Salary benchmarks
Our take
The analysis of business data is essential in making operational changes based on forecasting, managing supply chains, and optimizing revenue, but the mass of available data can be overwhelming. This can sometimes be combated with the power of AI-driven data analysis tools, but existing solutions are usually designed to solve specific problems within specialized industries.
Fractal develops AI-driven tools and platforms for businesses, including a conversational AI platform called Senseforth, a revenue growth AI tool called Samya, AI algorithms for medical imaging named Qure, and an all-in-one data analytics platform called Crux Intelligence. The company develops its solutions to be flexible and applicable within a wide range of industries including consumer packaged goods, healthcare, financial services, and media.
Whilst AI-driven business tools are commonplace, Fractal has taken the smart approach of developing a suite of flexible AI tools that can tackle problems in many industries, as opposed to focusing on a specific problem or industry. This strategy has positioned the company well for continued growth and impact, reflecting its broad applicability and potential in the market.
Steph
Company Specialist at Welcome to the Jungle
Insights
8% employee growth in 12 months
Company
Funding (last 2 of 4 rounds)
Jan 2022
$360m
GROWTH EQUITY VC
Jan 2019
$200m
GROWTH EQUITY VC
Total funding: $685m
Company HQ
World Trade Center, New York, NY
Articles
Leadership
Pranay Agrawal
(Co-Founder, CEO & EVP of Global Client Development)Pranay co-founded Fractal in 2001, serving as EV of Global Client Development since then and CEO since 2016.
Srikanth Velamakanni
(Co-Founder, Group Chief Executive & Executive Vice Chairman)After co-founding Fractal, Srikanth served as the CEO until moving into the role of Group Chief Executive & Executive Vice Chairman. He is also a Vice Chairperson at nasscom, Non Executive Chairman at ideaForge and co-founder & Trustee at Plaksha University.
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