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Join our team

We're looking for an experienced and passionate Data Engineer to join our team. Our vision is to empower Genesys to leverage data to drive better customer and business outcomes. Our batch and streaming solutions turn vast amounts of data into useful insights. If you’re interested in working with the latest big data technologies, using industry leading BI analytics and visualization tools, and bringing the power of data to our customers’ fingertips then this position is for you!

Our ideal candidate thrives in a fast-paced environment, enjoys the challenge of highly complex business contexts (that are typically being defined in real-time), and, above all, is a passionate about data and analytics.

What you'll get to do

• Work in an agile development environment, constantly shipping and iterating.
• Develop high quality batch and streaming big data pipelines.
• Interface with our Data Consumers, gathering requirements, and delivering complete data solutions.
• Own the design, development, and maintenance of datasets that drive key business decisions.
• Support, monitor and maintain the data models
• Adopt and define the standards and best practices in data engineering including data integrity, performance optimization, validation, reliability, and documentation.
• Keep up-to-date with advances in big data technologies and run pilots to design the data architecture to scale with the increased data volume using cloud services.
• Triage many possible courses of action in a high-ambiguity environment, making use of both quantitative analysis and business judgment.

Your experience should include

• Bachelor’s degree in CS or related technical field.
• 5+ years of experience in data modelling, data development, and data warehousing.
• Experience working with Big Data technologies (Hadoop, Hive, Spark, Kafka, Kinesis).
• Experience with large scale data processing systems for both batch and streaming technologies (Hadoop, Spark, Kinesis, Flink).
• Experience in programming using Python, Java or Scala.
• Experience with data orchestration tools (Airflow, Oozie, Step Functions).
• Solid understanding of database technologies including NoSQL and SQL.
• Strong in SQL queries (experience with Snowflake Cloud Datawarehouse is a plus)
• Work experience in Talend is a plus
• Track record of delivering reliable data pipelines with solid test infrastructure, CICD, data quality checks, monitoring, and alerting.
• Strong organizational and multitasking skills with ability to balance competing priorities.
• Excellent communication (verbal and written) and interpersonal skills and an ability to effectively communicate with both business and technical teams.
• An ability to work in a fast-paced environment where continuous innovation is occurring, and ambiguity is the norm.

Good to have

• Experience with AWS big data technologies - S3, EMR, Kinesis, Redshift, Glue

Technical Skills:

• Ability to understand and translate business requirements into design.
• Proficient in AWS infrastructure components such as S3, IAM, VPC, EC2, and Redshift.
• Experience in creating ETL jobs using Python/PySpark.
• Proficiency in creating AWS Lambda functions for event-based jobs.
• Knowledge of automating ETL processes using AWS Step Functions.
• Competence in building data warehouses and loading data into them.

Responsibilities:

• Understand business requirements and translate them into design.
• Assess AWS infrastructure needs for development work.
• Develop ETL jobs using Python/PySpark to meet requirements.
• Implement AWS Lambda for event-based tasks.
• Automate ETL processes using AWS Step Functions.
• Build data warehouses and manage data loading.
• Engage with customers and stakeholders to articulate the benefits of proposed solutions and frameworks.

We are looking for an outstanding ML Architect (Deployments) with expertise in deploying Machine Learning solutions/models into production and scaling them to serve millions of customers. A candidate with an adaptable and productive working style which fits in a fast-moving environment.

Skills:

- 5+ years deploying Machine Learning pipelines in large enterprise production systems.

- Experience developing end to end ML solutions from business hypothesis to deployment / understanding the entirety of the ML development life cycle.
- Expert in modern software development practices; solid experience using source control management (CI/CD).
- Proficient in designing relevant architecture / microservices to fulfil application integration, model monitoring, training / re-training, model management, model deployment, model experimentation/development, alert mechanisms.
- Experience with public cloud platforms (Azure, AWS, GCP).
- Serverless services like lambda, azure functions, and/or cloud functions.
- Orchestration services like data factory, data pipeline, and/or data flow.
- Data science workbench/managed services like azure machine learning, sagemaker, and/or AI platform.
- Data warehouse services like snowflake, redshift, bigquery, azure sql dw, AWS Redshift.
- Distributed computing services like Pyspark, EMR, Databricks.
- Data storage services like cloud storage, S3, blob, S3 Glacier.
- Data visualization tools like Power BI, Tableau, Quicksight, and/or Qlik.
- Proven experience serving up predictive algorithms and analytics through batch and real-time APIs.
- Solid working experience with software engineers, data scientists, product owners, business analysts, project managers, and business stakeholders to design the holistic solution.
- Strong technical acumen around automated testing.
- Extensive background in statistical analysis and modeling (distributions, hypothesis testing, probability theory, etc.)
- Strong hands-on experience with statistical packages and ML libraries (e.g., Python scikit learn, Spark MLlib, etc.)
- Experience in effective data exploration and visualization (e.g., Excel, Power BI, Tableau, Qlik, etc.)
- Experience in developing and debugging in one or more of the languages Java, Python.
- Ability to work in cross functional teams.
- Apply Machine Learning techniques in production including, but not limited to, neuralnets, regression, decision trees, random forests, ensembles, SVM, Bayesian models, K-Means, etc.

Roles and Responsibilities:

Deploying ML models into production, and scaling them to serve millions of customers.

Technical solutioning skills with deep understanding of technical API integrations, AI / Data Science, BigData and public cloud architectures / deployments in a SaaS environment.

Strong stakeholder relationship management skills - able to influence and manage the expectations of senior executives.
Strong networking skills with the ability to build and maintain strong relationships with both business, operations and technology teams internally and externally.

Provide software design and programming support to projects.

 Qualifications & Experience:

Engineering and post graduate candidates, preferably in Computer Science, from premier institutions with proven work experience as a Machine Learning Architect (Deployments) or a similar role for 5-7 years.

• Should have good hands-on experience in Informatica MDM Customer 360, Data Integration(ETL) using PowerCenter, Data Quality.
• Must have strong skills in Data Analysis, Data Mapping for ETL processes, and Data Modeling.
• Experience with the SIF framework including real-time integration
• Should have experience in building C360 Insights using Informatica
• Should have good experience in creating performant design using Mapplets, Mappings, Workflows for Data Quality(cleansing), ETL.
• Should have experience in building different data warehouse architecture like Enterprise,
• Federated, and Multi-Tier architecture.
• Should have experience in configuring Informatica Data Director in reference to the Data
• Governance of users, IT Managers, and Data Stewards.
• Should have good knowledge in developing complex PL/SQL queries.
• Should have working experience on UNIX and shell scripting to run the Informatica workflows and to control the ETL flow.
• Should know about Informatica Server installation and knowledge on the Administration console.
• Working experience with Developer with Administration is added knowledge.
• Working experience in Amazon Web Services (AWS) is an added advantage. Particularly on AWS S3, Data pipeline, Lambda, Kinesis, DynamoDB, and EMR.
• Should be responsible for the creation of automated BI solutions, including requirements, design,development, testing, and deployment