Data Engineer

• Design and build extraction, transformation, and loading (ETL) pipelines using Microsoft Fabric (Dataflow Gen2, Notebooks, or equivalent tools)
• Write optimized SQL queries and transformations for data ingestion from designated source systems
• Apply data quality rules and validation logic at each pipeline stage
• Implement incremental loads and manage refresh schedules for performance
• Escalate to Lead for architectural decisions or complex transformation patterns
• Define and implement data quality checks at ingestion, transformation, and output stages
• Perform ongoing data validation to ensure pipeline outputs align with business logic and source system expectations
• Identify, document, and escalate data quality issues with root cause analysis
• Maintain data quality dashboards and SLA monitoring
• Support UAT for new data sources or transformation logic
• Build and maintain data transformations using Power Query, SQL, or Python as appropriate
• Develop dimensional models and define aggregation logic aligned with analytics requirements
• Optimize data structures for performance and maintainability
• Document transformation logic, lineage, and assumptions per team standards
• Troubleshoot pipeline failures and performance issues; coordinate resolution with IT/Engineering
• Respond to data discrepancy reports from business users and analysts
• Maintain documentation of data sources, data dictionaries, and transformation specifications
• Support capacity planning and optimization of Fabric environments and pipelines
• Collaborate with Lead to define semantic models and calculated metrics

• Design, develop, and optimize enterprise-scale data pipelines and ETL/ELT workflows using Azure and Databricks technologies.
• Architect and implement scalable data ingestion, transformation, and orchestration processes using Azure Data Factory, Databricks, and Azure Synapse Analytics.
• Develop high-performance data transformation frameworks using PySpark, Python, and Spark SQL for large-scale distributed data processing.
• Optimize SQL queries, Spark jobs, and data workflows to improve performance, scalability, and cost efficiency.
• Lead data migration initiatives, including SQL Server migrations and modernization of legacy data platforms.
• Implement and maintain Delta Lake architecture, incremental data loading strategies, and enterprise data lake best practices.
• Collaborate with architects and cross-functional teams to design robust and scalable data models aligned with business and governance standards.
• Monitor and troubleshoot production pipelines, perform root-cause analysis, and implement preventive measures for recurring issues.
• Support CI/CD implementation and infrastructure automation for data engineering workflows.
• Mentor junior engineers and contribute to engineering standards, reusable frameworks, and technical best practices.
• Create and maintain technical documentation including architecture diagrams, pipeline documentation, and operational runbooks.
• Evaluate and recommend modern data engineering tools, frameworks, and optimization strategies.

L2 Data Engineer

• End-to-end pipeline work: MongoDB CDC → ingestion → lakehouse → warehouse → dbt → semantic layer → BI/AI consumers
• Co-ownership of architecture decisions with the Data Engineering Lead
• CDC from production MongoDB without degrading operational DB performance; ingestion patterns that make adding a new source a config change, not a project
• Orchestration that’s observable end-to-end (Airflow, Dagster, or Prefect)
• The dbt project: structure, conventions, tests, contracts, exposures, CI
• The semantic / metrics layer (dbt Semantic Layer, Cube, or equivalent) — one canonical definition per business metric
• LLM-readiness: column-level documentation, PII tagging, query cost guardrails, materialized metric tables, and evals on AI-generated SQL
• Migration of existing logic out of the Tableau and Metabase sprawl into modeled, governed sources

Senior Data Engineer

• Responsible for designing, building, and maintaining the data architecture and infrastructure required for our organization's data needs
• Develop, test, and deploy data solutions, ensuring they meet our organization's needs

Data Engineer, L2

• Responsible for designing, building, and maintaining the data architecture and infrastructure required for our organization's data needs.
• Develop, test, and deploy data solutions, ensuring they meet our organization's needs.

• Designing and implementing data architectures
• Data modeling
• Collaboration with Data Engineers and Data Analysts

Data Architect

• - Lead the design, development, and maintenance of end-to-end data analytical solutions using Microsoft on-premises and cloud technologies.
• - Engage in collaborative requirement elicitation activities with our valued clientele.
• - Mentor and guide junior team members in the field of data engineering.
• - Showcase your expertise by delivering innovative analytical solutions to our clients and contributing to knowledge transfer within the organization.

• Perform data transformation and integration tasks to ensure data from various sources are accurately processed and made ready for annotation projects and for delivery to clients per client requirements.
• Ensure efficient data storage, retrieval, and optimization to support data annotation workflows.
• Implement data quality checks and validation processes to ensure the accuracy, consistency, and integrity of data used in annotation projects.
• Collaborate with the Data Engineering team to design and implement robust and scalable data pipelines for importing and exporting data used in our data annotation projects.
• Identify and address performance bottlenecks in data pipelines to enhance the speed and efficiency of data import and export processes.
• Continuously seek opportunities to automate manual processes and improve data annotation workflows for increased productivity.
• Work closely with cross-functional teams, including data annotation teams, backend developers, and project managers, to understand project requirements and provide timely data support.
• Maintain comprehensive documentation of data pipelines, processes, and data structures to facilitate knowledge sharing and seamless project handovers.
• Address and resolve data-related issues, providing technical support to data annotation teams when required.
• Stay abreast of industry trends, tools, and technologies related to data engineering, and propose innovative solutions for data annotation projects.

• Build a centralized data lake on GCP by integrating diverse data sources
• Develop, maintain, and optimize SPARK-powered data processing pipelines
• Design and implement data validation and quality checks
• Collaborate with Data Science and ML teams for analytics
• Collaborate with cross-functional teams for data insights
• Design, develop, and maintain data dashboards
• Manage ETL/ELT processes and troubleshoot data issues

Mid/Senior Data Engineer

• Define and own PetroApp’s data strategy, roadmap, and operating model.
• Build and scale the company’s data foundation, including ingestion, transformation, modeling, warehousing, and self-service analytics.
• Create a single source of truth for key business and product metrics.
• Partner with Product and Engineering to improve instrumentation, event tracking, experimentation, and measurement.
• Partner with Commercial, Operations, Finance, and Leadership to deliver actionable insights and decision-ready dashboards.
• Design KPI frameworks for areas such as customer growth, retention, transaction performance, service adoption, operational efficiency, fraud/leakage, and profitability.
• Build dashboards, reporting layers, and analytical models that support both day-to-day execution and executive decision-making.
• Establish strong data governance practices, including data quality checks, lineage, documentation, definitions, access controls, and privacy standards.
• Identify and prioritize high-value use cases for advanced analytics, forecasting, anomaly detection, and automation.
• Lead root-cause analysis on business or operational issues and convert findings into measurable actions.
• Hire, mentor, and grow a lean, high-performing data team over time.
• Act as the internal champion for a strong data culture across PetroApp.

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