The Anblicks Data Engineer interview guide, interview questions, salary data, and interview experiences.

Anblicks Data Engineer Interview Guide

Getting ready for an Data Engineer interview at Anblicks? The Anblicks Data Engineer interview span across 10 to 12 different question topics. In preparing for the interview:
<ul>
<li>Know what skills are necessary for Anblicks Data Engineer roles.</li>
<li>Gain insights into the Data Engineer interview process at Anblicks.</li>
<li>Practice real Anblicks Data Engineer interview questions.</li>
</ul>
Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Anblicks Data Engineer interview.


Getting ready for an Data Engineer interview at Anblicks? The Anblicks Data Engineer interview span across 10 to 12 different question topics. In preparing for the interview:

- Know what skills are necessary for Anblicks Data Engineer roles.
- Gain insights into the Data Engineer interview process at Anblicks.
- Practice real Anblicks Data Engineer interview questions.

Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Anblicks Data Engineer interview.
        

Overview

Anblicks Data Engineer Salary

We've gathered this data from parsing thousands of interview experiences sourced from members.

We've gathered this data from parsing thousands of interview experiences sourced from members.

Anblicks Data Engineer Interview Process

You are given a dictionary with a key-value of <code>{string: number}</code> where values in the dictionary could be duplicates. You are required to extract the unique values from the dictionary where the value occurred only once.

Return a list of values where they occur only once.

Note: You can return the values in any order.

Input:

<pre tabindex="0" class="chroma"><code>dictionary = {&#34;key1&#34;: 1, &#34;key2&#34;: 1, &#34;key3&#34;: 7, &#34;key4&#34;: 3, &#34;key5&#34;: 4, &#34;key6&#34;: 7}
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>find_unique_values(dictionary) -&gt; [3,4]

#Only 3 and 4 occurred once.
</code></pre>

Dictionary Unique Values

Given a table called <code>employees</code>, get the largest salary of any employee by department

Example:

Input:

<code>employees</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>department</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>largest_salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Write a query to get the largest salary of any employee by department

Largest Salary by Department

You’re tasked with designing a database system for Swipe Inc., a company that manages payments for software developers. Swipe manages a set of APIs so developers can abstract payment processes for their web service and transfer control over to Swipe’s APIs to manage payment security and finance handling (contacting credit card providers, etc.)

Your database system should store a collection of API keys with their attributes, as shown in the example below:

<pre tabindex="0" class="chroma"><code>{
 &#34;API_key&#34;:{
 &#34;Transaction_list&#34;:{
 &#34;User_ID&#34;:{
 &#34;Name&#34;:{
 &#34;f_name&#34;: 
 &#34;l_name&#34;: 
 }
 &#34;Card_Details&#34;:{
 &#34;Bank&#34;:
 &#34;Card_No&#34;:
 &#34;Expiry&#34;:
 &#34;CV&#34;:
 } 
 }
 }
 &#34;Curr_balance&#34;:
 }
}
</code></pre>

What are the functional and non-functional requirements for this database?

Determine the requirements for designing a database system to store payment APIs

Swipe Payment API

Anblicks Data Engineer Interview Questions

Description: The Data Engineer is responsible for building Data Engineering Solutions using next generation data techniques. The individual will be working directly with product owners, customers and technologists to deliver data products/solutions in a collaborative and agile environment. Responsibilities:<ul><li>Responsible for design and development of big data solutions. Partner with domain experts, product managers, analyst, and data scientists to develop Big Data pipelines in Hadoop</li><li>Responsible for moving all legacy workloads to cloud platform</li><li>Work with data scientist to build Client pipelines using heterogeneous sources and provide engineering services for data science applications</li><li>Ensure automation through CI/CD across platforms both in cloud and on-premises</li><li>Define needs around maintainability, testability, performance, security, quality and usability for data platform</li><li>Drive implementation, consistent patterns, reusable components, and coding standards for data engineering processes</li><li>Convert SAS based pipelines into languages like PySpark, Scala to execute on Hadoop and non-Hadoop ecosystems</li><li>Tune Big data applications on Hadoop and non-Hadoop platforms for optimal performance</li><li>Evaluate new IT developments and evolving business requirements and recommend appropriate systems alternatives and/or enhancements to current systems by analyzing business processes, systems and industry standards.</li><li>Applies in-depth understanding of how data analytics collectively integrate within the sub-function as well as coordinates and contributes to the objectives of the entire function.</li><li>Produces detailed analysis of issues where the best course of action is not evident from the information available, but actions must be recommended/taken.</li><li>Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency.</li></ul> Qualifications: <ul><li>8+ years of total IT experience</li><li>5+ years of experience with Hadoop (Cloudera)/big data technologies</li><li>Advanced knowledge of the Hadoop ecosystem and Big Data technologies Hands-on experience with the Hadoop eco-system (HDFS, MapReduce, Hive, Pig, Impala, Spark, Kafka, Kudu, Solr)</li><li>Experience on designing and developing Data Pipelines for Data Ingestion or Transformation using Java or Scala or Python.</li><li>Experience with Spark programming (pyspark or scala or java)</li><li>Expert level building pipelines using Apache Spark Familiarity with core provider services from AWS, Azure or GCP, preferably having supported deployments on one or more of these platforms</li><li>Hands-on experience with Python/Pyspark/Scala and basic libraries for machine learning is required;</li><li>Exposure to containerization and related technologies (e.g. Docker, Kubernetes)</li><li>Exposure to aspects of DevOps (source control, continuous integration, deployments, etc.)</li><li>Proficient in programming in Java or Python with prior Apache Beam/Spark experience a plus.</li><li>System level understanding - Data structures, algorithms, distributed storage & compute</li><li>Can-do attitude on solving complex business problems, good interpersonal and teamwork skills</li><li>Possess team management experience and have led a team of data engineers and analysts.</li><li>Experience in Snowflake is a plus.</li></ul> Education:<ul><li>Bachelor’s degree/University degree or equivalent experience</li></ul>

Skills & Experience<ul><li>4+ years of experience in data engineering .</li><li>Strong Python programming</li><li>Strong SQL knowledge and good understanding of different databases .</li><li>Good understanding of Data – Able to transform a business entity into a database entity.</li><li>Knowledge of SDLC – Source code control (git process), unit testing, release process, CI/CD pipelines</li><li>Knowledge of any cloud Getting hands dirty attitude towards learning new technologies and exploring the current trends</li></ul>· Good to have:<ul><li>Experience with data pipelines – Spark/PySpark</li><li>Experience with Databricks or Snowflake</li><li>Experience with data modelling.</li><li>Excellent problem-solving and analytical skills</li><li>Ability to work independently and as part of a team</li><li>Experience working in an Agile environment</li></ul>Excellent written and oral communication skills; Ability to communicate effectively with technical and non-technical staff.

Description: The Data Engineer is responsible for building Data Engineering Solutions using next generation data techniques. The individual will be working directly with product owners, customers and technologists to deliver data products/solutions in a collaborative and agile environment. Responsibilities: Responsible for design and development of big data solutions. Partner with domain experts, product managers, analyst, and data scientists to develop Big Data pipelines in Hadoop Responsible for moving all legacy workloads to cloud platform Work with data scientist to build Client pipelines using heterogeneous sources and provide engineering services for data science applications Ensure automation through CI/CD across platforms both in cloud and on-premises Define needs around maintainability, testability, performance, security, quality and usability for data platform Drive implementation, consistent patterns, reusable components, and coding standards for data engineering processes Convert SAS based pipelines into languages like PySpark, Scala to execute on Hadoop and non-Hadoop ecosystems Tune Big data applications on Hadoop and non-Hadoop platforms for optimal performance Evaluate new IT developments and evolving business requirements and recommend appropriate systems alternatives and/or enhancements to current systems by analyzing business processes, systems and industry standards. Applies in-depth understanding of how data analytics collectively integrate within the sub-function as well as coordinates and contributes to the objectives of the entire function. Produces detailed analysis of issues where the best course of action is not evident from the information available, but actions must be recommended/taken. Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency. Qualifications: 8 years of total IT experience 5 years of experience with Hadoop (Cloudera)/big data technologies Advanced knowledge of the Hadoop ecosystem and Big Data technologies Hands-on experience with the Hadoop eco-system (HDFS, MapReduce, Hive, Pig, Impala, Spark, Kafka, Kudu, Solr) Experience on designing and developing Data Pipelines for Data Ingestion or Transformation using Java or Scala or Python. Experience with Spark programming (pyspark or scala or java) Expert level building pipelines using Apache Spark Familiarity with core provider services from AWS, Azure or GCP, preferably having supported deployments on one or more of these platforms Hands-on experience with Python/Pyspark/Scala and basic libraries for machine learning is required; Exposure to containerization and related technologies (e.g. Docker, Kubernetes) Exposure to aspects of DevOps (source control, continuous integration, deployments, etc.) Proficient in programming in Java or Python with prior Apache Beam/Spark experience a plus. System level understanding - Data structures, algorithms, distributed storage & compute Can-do attitude on solving complex business problems, good interpersonal and teamwork skills Possess team management experience and have led a team of data engineers and analysts. Experience in Snowflake is a plus. Education: Bachelor's degree/University degree or equivalent experience

Job Title: Senior Data Engineer JOB DUTIES: Participate in daily agile and scrum processes to understand changing business requirements, including examining system configurations and operating procedures, as well as functional requirements gathering. Ingest and prepare business-ready data in the cloud using Azure Data Factory (ADF) to build ELT(Extract Load and Transform)/ETL (Extract Transform and Load) data pipelines, then move the data into a data warehouse (Dedicated SQL Pool) and create data lake zones for data analytics and visualization. Work with a combination of Azure Data Factory and Azure Databricks, extract, load, and transform data from cloud sources and on-premises databases such as Oracle, SAP, and SQL Server to Data Lake Storage and Azure Synapse Analytics. Create an Azure Data Factory Pipeline Template to migrate an on-premises data platform to the Azure Cloud using batch processing methods by incremental or full load, and an ADF config driven framework to pull data from multiple sources with different table structures with less manual work and less resource usage. Analyze, design, and build modern data solutions using Azure PaaS services to support data visualization. Understand the current production state of the application and the impact of new implementation on existing business processes. Enable private end point, firewall setting and Azure Key Vault for robust data security. Analyze the existing SSIS packages and integrate it with Azure Data Factory, using SSIS transformations like Lookup, Derived column, Data conversion, Aggregate, Conditional split, SQL task, Script task, and Send Mail task. Create a JSON structure for data storage in Azure Cosmos DB (SQL API), write stored procedures and functions and work with the API team to create Cosmos DB queries that use fewer request units. Data Model in Snowflake and ELT using Snowflake SQL, implementing complex stored procedures and best practices with data warehouse and ETL concepts. Design and customize dimension data models for data warehouse supporting data using Azure Synapse Analytics, and select the appropriate distribution method in dimension and fact tables to load the data in an optimized manner, as well as implement complex stored procedures and best practices with data warehouse. Build a distributed in-memory application using spark applications and perform analytics efficiently on large datasets using python and Spark SQL, also use Spark SQL to implement transformation logic in Databricks and mount/unmount Azure Blob Storage. Read the data from different file format parquet, avro, csv and json using pySpark (Python API) in Azure Databricks and perform data extraction, transformation to uncover insights into customer usage patterns and insert curated data into a data warehouse. Create data visualization reports and dashboards in Power BI using data from the data warehouse, flat files, and Azure SQL. Responsible for fixing problems and conducting investigations into SQL queries, Stored Procedures related to long running jobs and Azure service performance. Utilize Azure Monitor and Alert services, create monitors, alerts, and notifications for Data Factory, Synapse Analytics, and Data Lake Storage. Perform the required daily GIT support for various projects. Be in charge of maintaining GIT repositories and access control procedures. Create CI&CD using azure dev ops pipeline to deploy Azure Services (Storage, Data factory, Key vault & Logic App) using ARM Templates. JOB REQUIREMENT: Master's degree in Computer Science, Computer Information Systems, or Engineering related or Technical related fields plus 2 years of experience. In lieu of the above, we will also accept a Bachelor's degree in Computer Science, Computer Information Systems, or Engineering related or Technical related fields plus 5 years of progressively responsible post-baccalaureate experience. Foreign degree equivalent is acceptable. We will also accept any suitable combination of education, training and/or experience. Experience to include working on Azure Data Factory (ADF), Oracle, SQL server, Azure Databricks, Azure Synapse Analytics, Data Lake Storage, Azure PaaS services, SSIS packages, Azure Cosmos DB (SQL API), Python, Spark applications, Azure SQL, SQL queries, Azure Monitor and Alert services, GIT Support, ARM Templates. HOURS: M-F, 8:00 a.m. - 5:00 p.m. JOB LOCATION: Dallas, Texas. Travel is not required, but candidates must be willing to relocate to unanticipated work locations across the country per contract demand. CONTACT: Email resume referencing job code# SDE01302023ANB to Maruthi Technologies INC. DBA Anblicks at [email protected]

The Data Engineer is responsible for building Data Engineering Solutions using next generation data techniques. The individual will be working directly with product owners, customers and technologists to deliver data products/solutions in a collaborative and agile environment.Responsibilities:Responsible for design and development of big data solutions. Partner with domain experts, product managers, analyst, and data scientists to develop Big Data pipelines in HadoopResponsible for moving all legacy workloads to cloud platformWork with data scientist to build Client pipelines using heterogeneous sources and provide engineering services for data science applicationsEnsure automation through CI/CD across platforms both in cloud and on-premisesDefine needs around maintainability, testability, performance, security, quality and usability for data platformDrive implementation, consistent patterns, reusable components, and coding standards for data engineering processesConvert SAS based pipelines into languages like PySpark, Scala to execute on Hadoop and non-Hadoop ecosystemsTune Big data applications on Hadoop and non-Hadoop platforms for optimal performanceEvaluate new IT developments and evolving business requirements and recommend appropriate systems alternatives and/or enhancements to current systems by analyzing business processes, systems and industry standards.Applies in-depth understanding of how data analytics collectively integrate within the sub-function as well as coordinates and contributes to the objectives of the entire function.Produces detailed analysis of issues where the best course of action is not evident from the information available, but actions must be recommended/taken.Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency. Qualifications: 8+ years of total IT experience5+ years of experience with Hadoop (Cloudera)/big data technologiesAdvanced knowledge of the Hadoop ecosystem and Big Data technologies Hands-on experience with the Hadoop eco-system (HDFS, MapReduce, Hive, Pig, Impala, Spark, Kafka, Kudu, Solr)Experience on designing and developing Data Pipelines for Data Ingestion or Transformation using Java or Scala or Python.Experience with Spark programming (pyspark or scala or java)Expert level building pipelines using Apache Spark Familiarity with core provider services from AWS, Azure or GCP, preferably having supported deployments on one or more of these platformsHands-on experience with Python/Pyspark/Scala and basic libraries for machine learning is required;Exposure to containerization and related technologies (e.g. Docker, Kubernetes)Exposure to aspects of DevOps (source control, continuous integration, deployments, etc.)Proficient in programming in Java or Python with prior Apache Beam/Spark experience a plus.System level understanding - Data structures, algorithms, distributed storage & computeCan-do attitude on solving complex business problems, good interpersonal and teamwork skillsPossess team management experience and have led a team of data engineers and analysts.Experience in Snowflake is a plus. Education:Bachelor’s degree/University degree or equivalent experience

Role PurposeWe are looking for a highly motivated and experienced Data Engineer to join our team of data experts. The ideal candidate will have a strong background in designing, Development processes using technologies such as Python, Spark, PySpark, SQL and NoSQL. As a Data Engineer, you will work closely with the Lead Data Engineer and Data Architect to implement end-to-end data solutions, build, and maintain data pipelines, and ensure the quality and integrity of our organization's data. Role Responsibilities· Collaborate with the Lead Data Engineer and Data Architect to design and implement end-to-end data solutions· Develop and maintain data pipelines and ETL processes using Python, Spark, and SQL· Ensure data quality and integrity by developing and implementing data validation and testing strategies· Implement data security measures to protect the confidentiality, integrity, and availability of our organization's data· Continuously monitor and evaluate the performance of data systems and make recommendations for improvements· Stay up-to-date with the latest data technologies and trends and provide recommendations for their adoption· Design and create processes for contextualizing, cleaning, aggregating, and transforming data, making it better and more useful to the business· Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics Skills & Experience· 5+ years of experience in data engineering and data architecture· Strong Python/Java programming· Strong SQL knowledge and good understanding of different databases (SQL and NoSQL)· Good understanding of Data – Able to transform a business entity into a database entity.· Knowledge of SDLC – Source code control (git process), unit testing, release process, CI/CD pipelines· Knowledge of any cloud – Storage, Network, Security and Compute· Getting hands dirty attitude towards learning new technologies and exploring the current trends· Good to have:o Experience with data pipelines – Spark/PySparko Experience with Databricks or Snowflakeo Experience with data streaming – Kafka · Experience with data modelling.· Excellent problem-solving and analytical skills· Ability to work independently and as part of a team· Experience working in an Agile environment· Excellent written and oral communication skills; Ability to communicate effectively with technical and non-technical staff.

Requirement: •10+ years of overall experience developing data solutions leveraging Microsoft technologies (SSIS and or ADF, SSAS, SQL Server, Azure SQL/Synapse) Azure Databricks experience is must. •2+ years of overall experience developing end to end cloud native enterprise data platforms on Azure (Must have a solid understanding of cloud concepts: Storage, Compute, Network and Managed Services) •Ability to work with key stake holders (both functional and technical) to understand the requirements, create a technology proposal, evaluate it and help the implementation team •Very good data warehousing and data modeling skills (Kimball, Inmon, Star, Snowflake, Constellation) •Very good knowledge of the source and destination systems connectors •4+ years of experience working on SQL Data Warehouse and/or Azure Synapse •4+ years of experience working on SQL, T-SQL, Stored procedures, Functions •2+ years of experience working on SSIS •1+ years of experience building data lakes leveraging ADLS or Blob storage •1+ years of experience implementing solutions involving Storage and Compute separation •Python or C# experience is a nice to have •Power BI experience is a nice to have •Azure Analysis Services experience is a nice to have •Snowflake Cloud Data Platform experience is a nice to have

Anblicks Data Engineer Interview Guide

Overview

Anblicks Data Engineer Salary

Anblicks Data Engineer Interview Process

Anblicks Data Engineer Interview Questions

Anblicks Data Engineer Jobs