Role Description We are looking for a skilled individual to join our rapidly growing team at Bluelight Consulting. This position is ideal for someone who thrives in a fast-paced, dynamic environment where everyone's opinions and efforts are valued and appreciated. You will have the opportunity to contribute to challenging and meaningful projects, developing high-quality applications that stand out in the market. We value continuous learning, personal growth, and hard work, offering a collaborative environment that promotes professional development. If you are passionate about software development and eager to be part of a growing software consultancy, we invite you to apply and join us on this exciting journey. Qualifications - Experience with AWS services including but not limited to S3, Athena, EC2, EMR, and Glue. - Ability to solve any ongoing issues with operating the cluster. - Experience with the integration of data from multiple data sources. - Experience with various database technologies such as SQLServer, Redshift, Postgres, and RDS. - Experience with one or more of the following data integration platforms: Pentaho Kettle, SnapLogic, Talend OpenStudio, Jitterbit, Informatica PowerCenter, or similar. - Knowledge of best practices and IT operations in an always-up, always-available service. - Experience with or knowledge of Agile Software Development methodologies. - Excellent problem-solving and troubleshooting skills. - Excellent oral and written communication skills with a keen sense of customer service. - Experience with collecting/managing/reporting on large data stores. - Awareness of Data governance and data quality principles. - Well-versed in Business Analytics including basic metric building and troubleshooting. - Understand Integration architecture: application integration and data flow diagrams, source-to-target mappings, data dictionary reports. - Familiar with Web Services: XML, REST, SOAP. - Experience with Git or similar version control software. - Experience with integrations with and/or use of BI tools such as GoodData (preferred), Tableau, PowerBI, or similar. - Broad experience with multiple RDBMS: MS SQLServer, Oracle, MySQL, PostgreSQL, Redshift. - Familiarity with SaaS/cloud data systems (e.g. Salesforce). - Data warehouse design: star-schemas, change data capture, denormalization. - SQL/DDL queries/Tuning techniques such as indexing, sorting, and distribution. - BS or MS degree in Computer Science or a related technical field. - 3+ years of Data Pipeline development such as SnapLogic or Datastage, Informatica, or related experience. - 3+ years of SQL experience (No-SQL experience is a plus). - Experience designing, building, and maintaining data pipelines. Requirements - Develop and maintain data models for core package application and reporting databases to describe objects and fields for support documentation and to facilitate custom application development and data integration. - Monitoring execution and performance of daily pipelines, triage and escalate any issues. - Collaborates with analytics and business teams to improve data models and data pipelines that feed business intelligence tools, increasing data accessibility and fostering data-driven decision-making across the organization. - Implements processes and systems to monitor data quality, ensuring production data is always accurate and available for key stakeholders and business processes. - Writes unit/integration tests, contributes to engineering wiki, and documents work. - Performs data analysis required to troubleshoot data-related issues and assist in the resolution of data issues. - Work within AWS/Linux cloud systems environment in support of data integration solution. - Works closely with a team of frontend and backend engineers, product managers, and analysts. - Teamwork: Collaborate with team members. Share knowledge, provide visibility of interpersonal accomplishments and follow directions when provided. Benefits - Competitive salary and bonuses, including performance-based salary increases. - Generous paid-time-off policy. - Flexible working hours. - Work remotely. - Continuing education, training, conferences. - Company-sponsored coursework, exams, and certifications.

• Architect & Lead Operational Data Flows by designing and overseeing the implementation of an Operational Data Store (ODS) • Build low-latency data streams using technologies like Kafka or Flink to power embedded analytics directly within customer-facing applications • Establish "Data Contracts" with upstream engineering teams to ensure high availability and schema stability for all real-time operational flows • Own the transition and scaling of our Analytical Data Store (e.g., Snowflake), ensuring it is optimized for both performance and cost-efficiency • Modernize transformation layer by implementing robust ELT patterns and modular data modeling (using dbt and airflow) • Champion Data Governance, ensuring that every dashboard and report is backed by high-quality, audited, and well-documented data • Build the "Data Foundation" for Machine Learning, including development of Feature Stores and automated pipelines for model training and inference • Mentor and grow a high-performing engineering team, fostering a culture of "DataOps" where automation, testing, and observability are the default • Act as a strategic partner to Product and Executive leadership, translating complex technical roadmaps into clear business value

• Work closely with business areas to understand needs and translate requirements into scalable data solutions aligned with the organization’s objectives. • Conduct requirements gathering and map business processes and data flows between systems. • Define and validate business rules, metrics, KPIs and corporate indicators. • Design and evolve data models for Data Warehouse, Data Lake and Lakehouse environments. • Define dimensional modeling strategies, including facts, dimensions, granularity and relationships. • Develop data architectures using Azure Data Factory, Databricks, Data Lake and Power BI. • Ensure data quality, consistency and traceability across data pipelines. • Define standards for data ingestion, transformation, quality and availability across Bronze, Silver and Gold layers. • Serve as a technical reference for Data Engineering, BI and Analytics teams. • Support solution implementation, ensuring adherence to architectural and business definitions. • Produce documentation for data models, business rules and corporate indicators.

• Inherit, evaluate, and take full ownership of existing ETL/ELT pipelines — identifying what to preserve, improve, or replace based on performance, reliability, and long-term maintainability. • Design and build scalable pipeline improvements or net-new solutions where current practices fall short. • Monitor pipeline health, troubleshoot data quality issues, and proactively resolve performance and reliability problems. • Manage and evolve orchestration tooling with openness to adopting better alternatives as infrastructure needs grow. • Optimize query performance, pipeline efficiency, and resource utilization across Convo’s data environment. • Participate in testing, deployment, and monitoring practices that promote long-term reliability and scalability. • Develop and maintain scalable data transformation processes, schema design, and data models that support evolving business requirements. • Establish and evolve data quality testing frameworks - building practices that catch issues early and create lasting internal trust in our data. • Own data governance, documentation, lineage, version control, and data quality standards across the organization. • Serve as the primary internal resource for data engineering guidance and recommendations, helping set standards and informing data infrastructure decisions across the organization. • Work closely with the data analyst to translate business questions into reliable, queryable data structures. • Educate and guide non-technical stakeholders on how to work effectively with data, what is and isn’t feasible, and how to frame data requests clearly. • Explore and implement tooling to enable self-service data discovery for internal teams, reducing bottlenecks and empowering stakeholders to answer their own questions. • Collaborate with Product, Engineering, Finance, Operations, and Data Science stakeholders to support reporting, forecasting, and business intelligence needs. • Partner with Product and Engineering teams to integrate analytics, event tracking, and reporting into products and platforms. • Establish and document data engineering standards, workflows, and best practices at Convo — building a foundation that is sustainable, well-understood, and not dependent on any single person. • Contribute to improvements in data architecture, tooling, monitoring, automation, and engineering best practices. • Evaluate emerging technologies and tooling to improve efficiency, automation, and accessibility of data systems. • Maintain clear technical documentation and operational standards that support long-term maintainability. • Exercise sound technical judgment in balancing immediate business needs with long-term platform sustainability. • Maintain strong confidentiality and discretion when handling sensitive organizational, financial, operational, and employee data.