Data Engineer at Wallero Technologies Pvt. Ltd.

Requirements:

● Understanding our data sets and how to bring them together.

● Working with our engineering team to support custom solutions offered to the product development.

● Filling the gap between development, engineering and data ops.

● Creating, maintaining and documenting scripts to support ongoing custom solutions.

● Excellent organizational skills, including attention to precise details

● Strong multitasking skills and ability to work in a fast-paced environment

● 5+ years experience with Python to develop scripts.

● Know your way around RESTFUL APIs.[Able to integrate not necessary to publish]

● You are familiar with pulling and pushing files from SFTP and AWS S3.

● Experience with any Cloud solutions including GCP / AWS / OCI / Azure.

● Familiarity with SQL programming to query and transform data from relational Databases.

● Familiarity to work with Linux (and Linux work environment).

● Excellent written and verbal communication skills

● Extracting, transforming, and loading data into internal databases and Hadoop

● Optimizing our new and existing data pipelines for speed and reliability

● Deploying product build and product improvements

● Documenting and managing multiple repositories of code

● Experience with SQL and NoSQL databases (Casendra, MySQL)

● Hands-on experience in data pipelining and ETL. (Any of these frameworks/tools: Hadoop, BigQuery,

RedShift, Athena)

● Hands-on experience in AirFlow

● Understanding of best practices, common coding patterns and good practices around

● storing, partitioning, warehousing and indexing of data

● Experience in reading the data from Kafka topic (both live stream and offline)

● Experience in PySpark and Data frames

Responsibilities:

You’ll

● Collaborating across an agile team to continuously design, iterate, and develop big data systems.

● Extracting, transforming, and loading data into internal databases.

● Optimizing our new and existing data pipelines for speed and reliability.

● Deploying new products and product improvements.

● Documenting and managing multiple repositories of code.

Analytics Job Description

We are hiring an Analytics Engineer to help drive our Business Intelligence efforts. You will

partner closely with leaders across the organization, working together to understand the how

and why of people, team and company challenges, workflows and culture. The team is

responsible for delivering data and insights that drive decision-making, execution, and

investments for our product initiatives.

You will work cross-functionally with product, marketing, sales, engineering, finance, and our

customer-facing teams enabling them with data and narratives about the customer journey.

You’ll also work closely with other data teams, such as data engineering and product analytics,

to ensure we are creating a strong data culture at Blend that enables our cross-functional partners

to be more data-informed.

Role : DataEngineer 

Please find below the JD for the DataEngineer Role..

  Location: Guindy,Chennai

How you’ll contribute:

• Develop objectives and metrics, ensure priorities are data-driven, and balance short-

term and long-term goals

• Develop deep analytical insights to inform and influence product roadmaps and

business decisions and help improve the consumer experience

• Work closely with GTM and supporting operations teams to author and develop core

data sets that empower analyses

• Deeply understand the business and proactively spot risks and opportunities

• Develop dashboards and define metrics that drive key business decisions

• Build and maintain scalable ETL pipelines via solutions such as Fivetran, Hightouch,

and Workato

• Design our Analytics and Business Intelligence architecture, assessing and

implementing new technologies that fitting

• Work with our engineering teams to continually make our data pipelines and tooling

more resilient

Who you are:

• Bachelor’s degree or equivalent required from an accredited institution with a

quantitative focus such as Economics, Operations Research, Statistics, Computer Science OR 1-3 Years of Experience as a Data Analyst, Data Engineer, Data Scientist

• Must have strong SQL and data modeling skills, with experience applying skills to

thoughtfully create data models in a warehouse environment.

• A proven track record of using analysis to drive key decisions and influence change

• Strong storyteller and ability to communicate effectively with managers and

executives

• Demonstrated ability to define metrics for product areas, understand the right

questions to ask and push back on stakeholders in the face of ambiguous, complex

problems, and work with diverse teams with different goals

• A passion for documentation.

• A solution-oriented growth mindset. You’ll need to be a self-starter and thrive in a

dynamic environment.

• A bias towards communication and collaboration with business and technical

stakeholders.

• Quantitative rigor and systems thinking.

• Prior startup experience is preferred, but not required.

• Interest or experience in machine learning techniques (such as clustering, decision

tree, and segmentation)

• Familiarity with a scientific computing language, such as Python, for data wrangling

and statistical analysis

• Experience with a SQL focused data transformation framework such as dbt

• Experience with a Business Intelligence Tool such as Mode/Tableau

Mandatory Skillset:

-Very Strong in SQL

-Spark OR pyspark OR Python

-Shell Scripting

1. Data Engineer

 Required skill set: AWS GLUE, AWS LAMBDA, AWS SNS/SQS, AWS ATHENA, SPARK, SNOWFLAKE, PYTHON

Mandatory Requirements  

• Experience in AWS Glue
• Experience in Apache Parquet 
• Proficient in AWS S3 and data lake 
• Knowledge of Snowflake
• Understanding of file-based ingestion best practices.
• Scripting language - Python & pyspark 

CORE RESPONSIBILITIES 

• Create and manage cloud resources in AWS 
• Data ingestion from different data sources which exposes data using different technologies, such as: RDBMS, REST HTTP API, flat files, Streams, and Time series data based on various proprietary systems. Implement data ingestion and processing with the help of Big Data technologies 
• Data processing/transformation using various technologies such as Spark and Cloud Services. You will need to understand your part of business logic and implement it using the language supported by the base data platform 
• Develop automated data quality check to make sure right data enters the platform and verifying the results of the calculations 
• Develop an infrastructure to collect, transform, combine and publish/distribute customer data.
• Define process improvement opportunities to optimize data collection, insights and displays.
• Ensure data and results are accessible, scalable, efficient, accurate, complete and flexible 
• Identify and interpret trends and patterns from complex data sets 
• Construct a framework utilizing data visualization tools and techniques to present consolidated analytical and actionable results to relevant stakeholders. 
• Key participant in regular Scrum ceremonies with the agile teams  
• Proficient at developing queries, writing reports and presenting findings 
• Mentor junior members and bring best industry practices 

 QUALIFICATIONS 

• 5-7+ years’ experience as data engineer in consumer finance or equivalent industry (consumer loans, collections, servicing, optional product, and insurance sales) 
• Strong background in math, statistics, computer science, data science or related discipline
• Advanced knowledge one of language: Java, Scala, Python, C# 
• Production experience with: HDFS, YARN, Hive, Spark, Kafka, Oozie / Airflow, Amazon Web Services (AWS), Docker / Kubernetes, Snowflake  
• Proficient with
• Data mining/programming tools (e.g. SAS, SQL, R, Python)
• Database technologies (e.g. PostgreSQL, Redshift, Snowflake. and Greenplum)
• Data visualization (e.g. Tableau, Looker, MicroStrategy)
• Comfortable learning about and deploying new technologies and tools. 
• Organizational skills and the ability to handle multiple projects and priorities simultaneously and meet established deadlines. 
• Good written and oral communication skills and ability to present results to non-technical audiences 
• Knowledge of business intelligence and analytical tools, technologies and techniques.

Familiarity and experience in the following is a plus:  

• AWS certification
• Spark Streaming 
• Kafka Streaming / Kafka Connect 
• ELK Stack 
• Cassandra / MongoDB 
• CI/CD: Jenkins, GitLab, Jira, Confluence other related tools

Job Description:

We are looking for a Big Data Engineer who have worked across the entire ETL stack. Someone who has ingested data in a batch and live stream format, transformed large volumes of daily and built Data-warehouse to store the transformed data and has integrated different visualization dashboards and applications with the data stores.    The primary focus will be on choosing optimal solutions to use for these purposes, then maintaining, implementing, and monitoring them.

Responsibilities:

• Develop, test, and implement data solutions based on functional / non-functional business requirements.
• You would be required to code in Scala and PySpark daily on Cloud as well as on-prem infrastructure
• Build Data Models to store the data in a most optimized manner
• Identify, design, and implement process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Implementing the ETL process and optimal data pipeline architecture
• Monitoring performance and advising any necessary infrastructure changes.
• 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 experts to strive for greater functionality in our data systems.
• Proactively identify potential production issues and recommend and implement solutions
• Must be able to write quality code and build secure, highly available systems.
• Create design documents that describe the functionality, capacity, architecture, and process.
• Review peer-codes and pipelines before deploying to Production for optimization issues and code standards

Skill Sets:

• Good understanding of optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and ‘big data’ technologies.
• Proficient understanding of distributed computing principles
• Experience in working with batch processing/ real-time systems using various open-source technologies like NoSQL, Spark, Pig, Hive, Apache Airflow.
• Implemented complex projects dealing with the considerable data size (PB).
• Optimization techniques (performance, scalability, monitoring, etc.)
• Experience with integration of data from multiple data sources
• Experience with NoSQL databases, such as HBase, Cassandra, MongoDB, etc.,
• Knowledge of various ETL techniques and frameworks, such as Flume
• Experience with various messaging systems, such as Kafka or RabbitMQ
• Creation of DAGs for data engineering
• Expert at Python /Scala programming, especially for data engineering/ ETL purposes