Level AI

Optimizes call center data with AI

Senior Backend Engineer - Analytics

Data EngineeringConfirmed live in the last 24 hours

Full-timeNo salary listed

About the Job

Requirements

A B.E./B.Tech, M.E./M.Tech, or PhD from a tier 1 engineering institute, with relevant work experience at a top technology company.
At least 3 years of backend and infrastructure experience, with a strong track record in development, architecture, and design.
Hands-on experience with large-scale databases, high-scale messaging systems, and real-time job queues.
Experience navigating and understanding large-scale systems, complex codebases, and architectural patterns.
Proven experience building high-scale data platforms.
Strong expertise in data warehouse schema design, including star schemas, fact tables, and dimensions.
Experience with data movement, transformation, and integration tools for propagation across systems.
Ability to evaluate and implement best practices in data architecture for scalable solutions.
Skills in designing data warehouse schemas, including star and snowflake schemas, fact and dimension tables, and normalization and denormalization techniques.
Proficiency with data warehousing and storage platforms such as Snowflake, Amazon Redshift, Google BigQuery, and Azure Synapse Analytics.
Expertise with ETL/ELT tools such as Apache NiFi, Apache Airflow, Informatica, Talend, and dbt.
Advanced SQL skills for complex queries, indexing, and performance tuning.
Strong programming skills in Python or Java for custom data pipelines and advanced data transformations.
Experience with real-time data integration tools such as Apache Kafka, Apache Spark, AWS Glue, Fivetran, and Stitch.
Familiarity with workflow automation tools such as Apache Airflow and Luigi for orchestrating and monitoring data pipelines.
Knowledge of API-based integrations, especially for aggregating data from distributed sources.

Responsibilities

Design and implement analytical platforms that provide insightful dashboards to customers.
Develop and maintain data warehouse schemas, such as star schemas, fact tables, and dimensions, to support efficient querying and data access.
Oversee data propagation processes from source databases to warehouse-specific databases and tools, ensuring data accuracy, reliability, and timeliness.
Ensure the architectural design is extensible and scalable to adapt to future needs.

Desired Qualifications

Experience with Google Cloud, Django, PostgreSQL, Celery, and Redis.
Some experience with AI infrastructure and operations.

Salary Range: 0 to 0 (Currency: INR) (Pay period: per-year-salary)

Level AI was founded in 2019 and is a Series C startup headquartered in Mountain View, California. Level AI revolutionises customer engagement by transforming contact centres into strategic assets. Our AI-native platform leverages advanced technologies such as Large Language Models to extract deep insights from customer interactions. By providing actionable intelligence, Level AI empowers organisations to enhance customer experience and drive growth. Consistently updated with the latest AI innovations, Level AI stands as the most adaptive and forward-thinking solution in the industry.

Competencies:

Data Modelling: Skilled in designing data warehouse schemas (e.g., star and snowflake schemas), with experience in fact and dimension tables, as well as normalization and denormalization techniques.

Data Warehousing & Storage Solutions: Proficient with platforms such as Snowflake, Amazon Redshift, Google BigQuery, and Azure Synapse Analytics.

ETL/ELT Processes: Expertise in ETL/ELT tools (e.g., Apache NiFi, Apache Airflow, Informatica, Talend, dbt) to facilitate data movement from source systems to the data warehouse.

SQL Proficiency: Advanced SQL skills for complex queries, indexing, and performance tuning.

Programming Skills: Strong in Python or Java for building custom data pipelines and handling advanced data transformations.

Data Integration: Experience with real-time data integration tools like Apache Kafka, Apache Spark, AWS Glue, Fivetran, and Stitch.

Data Pipeline Management: Familiar with workflow automation tools (e.g., Apache Airflow, Luigi) to orchestrate and monitor data pipelines.

APIs and Data Feeds: Knowledgeable in API-based integrations, especially for aggregating data from distributed sources.

Responsibilities -

Design and implement analytical platforms that provide insightful dashboards to customers.
Develop and maintain data warehouse schemas, such as star schemas, fact tables, and dimensions, to support efficient querying and data access.
Oversee data propagation processes from source databases to warehouse-specific databases/tools, ensuring data accuracy, reliability, and timeliness.
Ensure the architectural design is extensible and scalable to adapt to future needs.

Requirement -

Qualification: B.E/B.Tech/M.E/M.Tech/PhD from tier 1 Engineering institutes with relevant work experience with a top technology company.
3+ years of Backend and Infrastructure Experience with a strong track record in development, architecture and design.
Hands-on experience with large-scale databases, high-scale messaging systems and real-time Job Queues.
Experience navigating and understanding large scale systems and complex code-bases, and architectural patterns.
Proven experience in building high-scale data platforms.
Strong expertise in data warehouse schema design (star schema, fact tables, dimensions).
Experience with data movement, transformation, and integration tools for data propagation across systems.
Ability to evaluate and implement best practices in data architecture for scalable solutions.

Nice to have:

Experience with Google Cloud, Django, Postgres, Celery, Redis.
Some experience with AI Infrastructure and Operations.

To learn more visit : https://thelevel.ai/

Funding : https://www.crunchbase.com/organization/level-ai

LinkedIn : https://www.linkedin.com/company/level-ai/

Our AI platform : https://www.youtube.com/watch?v=g06q2V_kb-s

About the Company

Level AI

Optimizes call center data with AI

Level AI provides software that improves customer experience and operational efficiency in the BPO sector by integrating human and machine intelligence. Its platform unifies call center data from multiple sources and uses intelligent automation and machine learning to automate tasks, guide decisions, and improve interactions, all delivered through a subscription model. It differentiates itself by offering end-to-end data integration across silos and blending human expertise with ML to boost sales conversions and QA productivity in call centers. The goal is to help BPO providers deliver better customer experiences while making operations more efficient and cost-effective.

Company size: 201-500
Company stage: Acquired
Total funding: $74.4M
Headquarters: Mountain View, CA
Founded: 2018

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