Data Engineer

Lightning Job By Cutshort ⚡

As part of this feature, you can expect status updates about your application and replies within 72 hours (once the screening questions are answered)

About Databook:-

- Great salespeople let their customers’ strategies do the talking.

Databook’s award-winning Strategic Relationship Management (SRM) platform uses advanced AI and NLP to empower the world’s largest B2B sales teams to create, manage, and maintain strategic relationships at scale. The platform ingests and interprets billions of financial and market data signals to generate actionable sales strategies that connect the seller’s solutions to a buyer’s financial pain and urgency.

The Opportunity

We're seeking Junior Engineers to support and develop Databook’s capabilities. Working closely with our seasoned engineers, you'll contribute to crafting new features and ensuring our platform's reliability. If you're eager about playing a part in building the future of customer intelligence, with a keen eye towards quality, we'd love to meet you!

Specifically, you'll

- Participate in various stages of the engineering lifecycle alongside our experienced engineers.

- Assist in maintaining and enhancing features of the Databook platform.

- Collaborate with various teams to comprehend requirements and aid in implementing technology solutions.

Please note: As you progress and grow with us, you might be introduced to on-call rotations to handle any platform challenges.

Working Arrangements:

- This position offers a hybrid work mode, allowing employees to work both remotely and in-office as mutually agreed upon.

What we're looking for

- 1-2+ years experience as a Data Engineer

- Bachelor's degree in Engineering

- Willingness to work across different time zones

- Ability to work independently

- Knowledge of cloud (AWS or Azure)

- Exposure to distributed systems such as Spark, Flink or Kafka

- Fundamental knowledge of data modeling and optimizations

- Minimum of one year of experience using Python working as a Software Engineer

- Knowledge of SQL (Postgres) databases would be beneficial

- Experience with building analytics dashboard

- Familiarity with RESTful APIs and/or GraphQL is welcomed

- Hand-on experience with Numpy, Pandas, SpaCY would be a plus

- Exposure or working experience on GenAI (LLMs in general), LLMOps would be a plus

- Highly fluent in both spoken and written English language

Ideal candidates will also have:

- Self-motivated with great organizational skills.

- Ability to focus on small and subtle details.

- Are willing to learn and adapt in a rapidly changing environment.

- Excellent written and oral communication skills.

Join us and enjoy these perks!

- Competitive salary with bonus

- Medical insurance coverage

- 5 weeks leave plus public holidays

- Employee referral bonus program

- Annual learning stipend to spend on books, courses or other training materials that help you develop skills relevant to your role or professional development

- Complimentary subscription to Masterclass

• Core Java: advanced level competency, should have worked on projects with core Java development.

• Linux shell : advanced level competency, work experience with Linux shell scripting, knowledge and experience to use important shell commands

• Rdbms, SQL: advanced level competency, Should have expertise in SQL query language syntax, should be well versed with aggregations, joins of SQL query language.

• Data structures and problem solving: should have ability to use appropriate data structure.

• AWS cloud : Good to have experience with aws serverless toolset along with aws infra

• Data Engineering ecosystem : Good to have experience and knowledge of data engineering, ETL, data warehouse (any toolset)

• Hadoop, HDFS, YARN : Should have introduction to internal working of these toolsets

• HIVE, MapReduce, Spark: Good to have experience developing transformations using hive queries, MapReduce job implementation and Spark Job Implementation. Spark implementation in Scala will be plus point.

• Airflow, Oozie, Sqoop, Zookeeper, Kafka: Good to have knowledge about purpose and working of these technology toolsets. Working experience will be a plus point here.

• Hands-on experience in any Cloud Platform

• Microsoft Azure Experience

• Developing telemetry software to connect Junos devices to the cloud
• Fast prototyping and laying the SW foundation for product solutions
• Moving prototype solutions to a production cloud multitenant SaaS solution
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources
• Build analytics tools that utilize the data pipeline to provide significant insights into customer acquisition, operational efficiency and other key business performance metrics.
• Work with partners including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
• Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
• Work with data and analytics specialists to strive for greater functionality in our data systems.

Qualification and Desired Experiences

• Master in Computer Science, Electrical Engineering, Statistics, Applied Math or equivalent fields with strong mathematical background
• 5+ years experiences building data pipelines for data science-driven solutions
• Strong hands-on coding skills (preferably in Python) processing large-scale data set and developing machine learning model
• Familiar with one or more machine learning or statistical modeling tools such as Numpy, ScikitLearn, MLlib, Tensorflow
• Good team worker with excellent interpersonal skills written, verbal and presentation
• Create and maintain optimal data pipeline architecture,
• Assemble large, sophisticated data sets that meet functional / non-functional business requirements.
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Experience with AWS, S3, Flink, Spark, Kafka, Elastic Search
• Previous work in a start-up environment
• 3+ years experiences building data pipelines for data science-driven solutions
• Master in Computer Science, Electrical Engineering, Statistics, Applied Math or equivalent fields with strong mathematical background
• We are looking for a candidate with 9+ years of experience in a Data Engineer role, who has attained a Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field. They should also have experience using the following software/tools:
• Experience with big data tools: Hadoop, Spark, Kafka, etc.
• Experience with relational SQL and NoSQL databases, including Postgres and Cassandra.
• Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
• Experience with AWS cloud services: EC2, EMR, RDS, Redshift
• Experience with stream-processing systems: Storm, Spark-Streaming, etc.
• Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
• Strong hands-on coding skills (preferably in Python) processing large-scale data set and developing machine learning model
• Familiar with one or more machine learning or statistical modeling tools such as Numpy, ScikitLearn, MLlib, Tensorflow
• Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
• Experience building and optimizing ‘big data’ data pipelines, architectures and data sets.
• Experience performing root cause analysis on internal and external data and processes to answer specific business questions and find opportunities for improvement.
• Strong analytic skills related to working with unstructured datasets.
• Build processes supporting data transformation, data structures, metadata, dependency and workload management.
• A successful history of manipulating, processing and extracting value from large disconnected datasets.
• Proven understanding of message queuing, stream processing, and highly scalable ‘big data’ data stores.
• Strong project management and interpersonal skills.
• Experience supporting and working with multi-functional teams in a multidimensional environment.

• You'd have to set up your own shop, work with design customers to find generalizable use cases, and build them out.
• Ability to collaborate with cross-functional teams to build and ship new features
• At least 2-5 years of experience
• Predictive Analytics – Machine Learning Algorithms, Logistics & Linear Regression, Decision Tree, Clustering. 
• Exploratory Data Analysis – Data Preparation, Data Exploration, and Data Visualization. 
• Analytics Tools – R, Python, SQL, Power BI, MS Excel.

• 3-5yrs of practical DS experience working with varied data sets. Working with retail banking is preferred but not necessary.
• Need to be strong in concepts of statistical modelling – particularly looking for practical knowledge learnt from work experience (should be able to give "rule of thumb" answers)
• Strong problem solving skills and the ability to articulate really well.
• Ideally, the data scientist should have interfaced with data engineering and model deployment teams to bring models / solutions to "live" in production.
• Strong working knowledge of python ML stack is very important here.
• Willing to work on diverse range of tasks in building ML related capability on the Corridor Platform as well as client work.
• Someone with strong interest in data engineering aspect of ML is highly preferred, i.e. can play dual role of Data Scientist as well as someone who can code a module on our Corridor Platform writing robust code.

Structured ML techniques for candidates:

1. GBM
2. XgBoost
3. Random Forest
4. Neural Net
5. Logistic Regression