Senior Analytics Engineer

Role: Senior Analytics Engineer

Location: Bengaluru 

What you’ll do 

We’re MiQ, a global programmatic media partner for marketers and agencies. Our people are at the heart of everything we do, so you will be too. No matter the role or the location, we’re all united in the vision to lead the programmatic industry and make it better. 

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  

• Bachelor's, 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

We’ve highlighted some key skills, experience and requirements for this role. But please don’t worry if you don’t meet every single one. Our talent team strives to find the best people. They might see something in your background that’s a fit for this role or another opportunity at MiQ.  

If you have a passion for the role, please still apply.  

What impact will you create? 

The analytics engineer will create significant impact by:

• Optimizing Data Workflows: Enhancing the efficiency and performance of data analytics jobs, leading to faster insights and decision-making for the DnA department.
• Improving Data Quality and Accessibility: By creating a robust business layer of data, the analytics engineer ensures that business analysts and other stakeholders have reliable, well-structured data at their disposal.
• Enhancing Collaboration: Facilitating better collaboration between the data management, data engineering, and DnA teams will lead to more cohesive and effective data solutions.
• Driving Business Outcomes: The optimized data processes and improved data quality will directly contribute to more accurate and timely insights, ultimately leading to better business outcomes such as improved campaign performance, enhanced customer targeting, and more informed strategic decisions.
• Supporting Scalable Solutions: By creating a scalable and efficient data framework, the analytics engineer will help the organization adapt to growing data needs and complexity, ensuring long-term success in data-driven initiatives.

What’s in it for you? 

Our Center of Excellence is the very heart of MiQ, and it’s where the magic happens. It means everything you do and everything you create will have a huge impact on our entire global business.

MiQ is incredibly proud to foster a welcoming culture. We do everything possible to make sure everyone feels valued for what they bring. With global teams committed to diversity, equity, and inclusion, we’re always moving towards becoming an even better place to work. 

Values 

Our values are so much more than statements. They unite MiQers in every corner of the world. They shape the way we work and the decisions we make. And they inspire us to stay true to ourselves and to aim for better. Our values are there to be embraced by everyone so that we naturally live and breathe them. Just like inclusivity, our values flow through everything we do - no matter how big or small.

• We do what we love - Passion
• We figure it out - Determination
• We anticipate the unexpected - Agility
• We always unite - Unite
• We dare to be unconventional - Courage

Benefits  

Every region and office has specific perks and benefits, but every person joining MiQ can expect: 

• A hybrid work environment  
• New hire orientation with job-specific onboarding and training
• Internal and global mobility opportunities
• Competitive healthcare benefits
• Bonus and performance incentives
• Generous annual PTO paid parental leave, with two additional paid days to acknowledge holidays, cultural events, or inclusion initiatives.
• Employee resource groups are designed to connect people across all MiQ regions, drive action, and support our communities.

Apply today! 

Equal Opportunity Employer