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’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

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

You’re given a dataframe of students named <code>students_df</code>:

<code>students_df</code> table

<table>
<thead>
<tr>
<th>name</th>
<th>age</th>
<th>favorite_color</th>
<th>grade</th>
</tr>
</thead>

<tbody>
<tr>
<td>Tim Voss</td>
<td>19</td>
<td>red</td>
<td>91</td>
</tr>

<tr>
<td>Nicole Johnson</td>
<td>20</td>
<td>yellow</td>
<td>95</td>
</tr>

<tr>
<td>Elsa Williams</td>
<td>21</td>
<td>green</td>
<td>82</td>
</tr>

<tr>
<td>John James</td>
<td>20</td>
<td>blue</td>
<td>75</td>
</tr>

<tr>
<td>Catherine Jones</td>
<td>23</td>
<td>green</td>
<td>93</td>
</tr>
</tbody>
</table>
Write a function named <code>grades_colors</code> to select only the rows where the student’s favorite color is green or red and their grade is above 90.

Example:

Input:

<pre tabindex="0" class="chroma"><code>import pandas as pd

students = {&#34;name&#34; : [&#34;Tim Voss&#34;, &#34;Nicole Johnson&#34;, &#34;Elsa Williams&#34;, &#34;John James&#34;, &#34;Catherine Jones&#34;], &#34;age&#34; : [19, 20, 21, 20, 23], &#34;favorite_color&#34; : [&#34;red&#34;, &#34;yellow&#34;, &#34;green&#34;, &#34;blue&#34;, &#34;green&#34;], &#34;grade&#34; : [91, 95, 82, 75, 93]}

students_df = pd.DataFrame(students)
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def grades_colors(students_df) -&gt;
</code></pre>

<table>
<thead>
<tr>
<th>name</th>
<th>age</th>
<th>favorite_color</th>
<th>grade</th>
</tr>
</thead>

<tbody>
<tr>
<td>Tim Voss</td>
<td>19</td>
<td>red</td>
<td>91</td>
</tr>

<tr>
<td>Catherine Jones</td>
<td>23</td>
<td>green</td>
<td>93</td>
</tr>
</tbody>
</table>

Write a function to select only the rows where the student's favorite color is green or red and their grade is above 90.

Good Grades and Favorite Colors

Anblicks Data Engineer Interview Questions

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]

Experience- 4+ years Experience in hadoop/big data technologies.Experience in spark.Experience working on Google or AWS cloud developing data solutions. Certifications preferred.Hands-on experience with Python/Pyspark/Scala and basic libraries for machine learning is required;Experience with containerization and related technologies (e.g. Docker, Kubernetes)Experience with all aspects of DevOps (source control, continuous integration, deployments, etc.)Comprehensive knowledge of the principles of software engineering and data analyticsAdvanced 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)Knowledge of agile (scrum) development methodology is a plusStrong development/automation skillsProficient 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 skills

Skills:Azure, Snowflake, Airflow, Fivetran, SQL Stored Procedures, Power BI, Great Expectations DQ, Atlassian BitBucket, Jira, Confluence, Data Analysis. Description:<ul><li>Manage offshore development team and be the onsite SPOC for the client stakeholders.</li><li>Experience working with onsite business stakeholders to understand, analyze and translate the requirements to the development team.</li><li>Design and implement Snowflake data pipelines and data warehouses to support business intelligence, analytics, and machine learning initiatives.</li><li>Optimize data models and query performance to ensure efficient data access and analysis.</li><li>Collaborate with data analysts, and business stakeholders to understand their data requirements and translate them into technical solutions.</li><li>Mentor and guide junior data engineers in their professional development.</li><li>Stay up-to-date with the latest Snowflake features and best practices.</li><li>Contribute to the development of data engineering standards and methodologies.</li><li>Having experience in BI tools is an added advantage.</li><li>Having experience in data quality is an added advantage.</li></ul>

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]

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>

Anblicks Data Engineer Interview Guide

Overview

Anblicks Data Engineer Salary

Anblicks Data Engineer Interview Process

Anblicks Data Engineer Interview Questions

Anblicks Data Engineer Jobs