About Dsider
Dsider is an AI-powered decision intelligence platform for the energy sector. We help companies transform complexity into clarity using AI models, digital twins, and decision modeling tools — driving smarter, faster, and more profitable decisions across the energy value chain.
The role
We're looking for a Data Analytics Engineer to improve and build a product that delivers useful analytics to customers while owning the ETL pipelines and database design that power reliable platform analytics. You will validate that platform outputs are accurate, explainable, and useful for end users, and QA product accuracy by checking metrics, calculations, edge cases, and data assumptions against expected results. Over time, you will build subject-matter fluency in petroleum and heavy-industry operations, though prior domain knowledge is not required.
Key responsibilities
- Design, build, and maintain ETL/ELT pipelines that ingest, transform, and load industrial and operational data into the platform
- Design and evolve database schemas, tables, and models that support accurate, scalable analytics
- Use Python for data analysis, pipeline logic, validation scripts, and product accuracy checks
- Perform EDA and data quality analysis to characterize datasets, spot anomalies, and identify gaps before they affect product outputs
- Analyze how the platform handles real datasets and where accuracy, performance, or usability breaks down
- Test and validate product logic for operational and commercial metrics (e.g., production, downtime, throughput, cost)
- Reproduce accuracy issues, isolate root causes in data pipelines or schema design, and confirm fixes before release
- Partner with product and engineering to tighten definitions, improve data handling, and raise confidence in platform outputs
- Document data models, pipeline logic, assumptions, and validation checks so work is repeatable
- Help prioritize platform improvements based on impact to analytical correctness, data reliability, and user trust
Qualifications
- Strong analytical skills and comfort with imperfect, multi-source data
- Experience with EDA and data quality analysis (profiling, completeness, consistency, anomaly detection)
- Hands-on experience with ETL/ELT and SQL
- Expected fluency in Python for data work (analysis, transforms, automation, and validation)
- Experience designing relational databases / analytical schemas (normalization, keys, indexing)
- A QA mindset for analytical products: verifying logic, catching edge cases, and proving correctness
- Clear communication with product, engineering, and domain stakeholders
- Curiosity about industrial / petroleum production systems and willingness to learn the domain on the job
- Experience using AI tools and building agentic workflows. RAG experience is a plus.
- Ability to build reliable ETL pipelines and well-designed databases that support trustworthy analytics
- Strong focus on analytical accuracy, with the ability to identify, investigate, and prevent recurring data and logic issues
- Ability to quickly detect, troubleshoot, and resolve incorrect metrics, data-quality issues, and edge-case failures
- Willingness and ability to develop fluency in petroleum and heavy-industry operations while learning the domain on the job
- Bonus (not required): cloud data warehouses, orchestration tools, or pipeline frameworks
- Bonus (not required): background in manufacturing, energy, oil & gas, or other heavy industry
Nice to have
- Familiarity with time-series or sensor/operational data
- Experience writing acceptance criteria or validation checklists for analytics products
- Basic statistical methods for variance checks, anomaly detection, or forecast validation
- Comfort working closely with domain experts and customer-facing product teams
Work model
- Location
- United States
- Work model
- Remote, with preference for candidates in Houston
- Travel
- Minimal