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Engaged Employer

MiQ Digital

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3.3

based on 91 Reviews

17 MiQ Digital Jobs

Analytics Engineer

MiQ Digital

3.3

based on 91 Reviews

6-7 years

Bangalore / Bengaluru

1 vacancy

Analytics Engineer

MiQ Digital

posted 5mon ago

Job Role Insights

Flexible timing

Key skills for the job

Digital Marketing Data Analytics SQL Healthcare Operations Clinical Data Management

+ 4 more

Job Description

As an Analytics Engineer within MiQ DnA department, you will play a pivotal role in creating and optimising the business layer of data. You will continue to be tech and Business focussed. Your responsibilities will revolve around designing data models, data normalisation and optimization within data pipelines, maintaining data documentation, ensuring that the data is readily consumable by dashboards, data science models, and automated jobs.

You will also work closely with other analysts to guide and enable them on code optimization, necessary training to follow best practices at all times.

Additionally, you will collaborate closely with the Data Management (DM) team, serving as the Single Point of Contact (SPOC) from the DnA team, to facilitate seamless communication and alignment of objectives.

Key Responsibilities:

Design Data models, Data Normalisation and Optimization:

Develop and implement strategies for data normalisation and optimization within the existing data pipelines.
Ensure consistency and accuracy of data across various sources and formats.
Own data transformation - Model raw data into clean, tested, and reusable datasets for all formats and attributes of data
Generate accurate models and communicate effectively through visual representations

Business Layer Development:

Build and maintain a robust business layer of data that aligns with DnA insights and solutions.
Collaborate with stakeholders to understand business needs and translate them into actionable data structures.
Own and drive data documentation (providing identifiable and understandable descriptions of data, so as to easily find them during querying) for DnA team
Create re-usable data assets ready for analysis by DnA team

Pipeline Maintenance:

Continuously improve data pipelines to enhance efficiency, reliability, and scalability.
Troubleshoot and resolve pipeline issues in a timely manner to minimise disruptions.
Apply software engineering best practices to analytics code

Collaboration with Data Science and Local Product Teams:

Work closely with analysts, data scientists and consultants to integrate data solutions into dashboards, models, and automated processes.
Provide support in developing and optimising data-driven solutions.
Continue to always bring analytical and business-outcomes mindset to the efforts of Data engineering

Optimization of High-Cost Jobs and Data Quality:

Identify opportunities and optimise high-cost data processing and analysis tasks within DnA.
Implement solutions to improve performance and reduce resource consumption.
Define data quality metrics to be used and measured for for operational and analytics fit needs
Write data cleansing algorithms when needed to further improve the quality of data

Single Point of Contact for Data Management Team:

Act as the primary liaison between the DnA team and the Data Management team.
Facilitate communication, coordinate priorities, and ensure alignment of objectives.
Liaise with Data Management team to build tools and infrastructure to support the efforts of DnA and Data Management team as a whole

Best Practices to own:

Version control to trace the history of changes in datasets and roll back to older versions if something goes wrong
Data unit testing to examine small chunks of data transformations for quality and correspondence to the set tasks.
Continuous integration and continuous delivery (CI/CD) to ensure up-to-date and reliable data.
Align business requirements with data assets at all times
Help us increase the amount of insight we can draw from our existing data
Training and sharing best practices within DnA

Who are your stakeholders?

The primary stakeholders for the analytics engineer role include:

Data Management Team: They ensure that the data is well-organized, clean, and accessible. The analytics engineer will collaborate with them to optimize data pipelines and structures.
Data Engineering Team: This team is responsible for building and maintaining the data infrastructure. The analytics engineer will work closely with them to ensure that the business layer of data is well-integrated and performs efficiently.
DnA Department (Data & Analytics): As the direct beneficiaries, the DnA department relies on optimized data workflows and accurate business layers to deliver actionable insights.
Business Analysts: They will use the business layer of data to generate insights and reports. Their ability to efficiently perform their roles will depend on the quality of the data layers created.
Senior Leadership: They are interested in the overall performance and efficiency of the DnA department, which the analytics engineer will help improve through data optimization and integration efforts.

What you ll bring

Bachelors, master s, or PhD degree in corresponding domains, e.g., statistics, mathematics, computer science, software engineering, or IT.
6-7 years of experience into analytics, data engineer, BI engineer etc.
Proven experience in data engineering, data normalization, and optimization.
Proficiency in data pipeline development using tools such as Apache Spark.
Strong understanding of data modelling concepts and techniques.
Excellent communication and collaboration skills, with the ability to effectively interact with cross-functional teams.
Strong problem-solving abilities and attention to detail.
Familiarity with machine learning concepts and algorithms is a plus.
Experience in digital advertising/digital marketing companies/projects is a plus.
Hands on working experience of DataOps methodology is highly preferred
Extensive experience working in the data space
Good knowledge of SQL, Python, R, PySpark, dbt (implementing analytics code using SQL), Git, cloud warehouses
Expanding more on above, we need candidates with hands-on experience in data warehouses like Snowflake, Amazon Redshift, and Google BigQuery; ETL tools like AWS Glue, Talend, or others; Business Intelligence and Data visualisation tools like Tableau , Looker, or equivalent.
Proven experience in working knowledge of how to adopt software engineering best practices and apply them to analytics code
Extensive hands-on experience with tools for building data pipelines
Strong interpersonal skills - Being able to ask the right questions in an appropriate way is crucial