What We’re Looking For

We are hiring a seasoned expert who walks in, understands our ecosystem, identifies gaps, and drives measurable outcomes—without needing to be told what to do next.

This is a hands-on, high-ownership engagement. You will be embedded into our data team to assess the current state of our data pipelines, identify and fix inefficiencies, and build a more reliable, scalable, and insight-ready data foundation. The right candidate combines deep technical expertise with business acumen and a strong bias toward delivering working solutions over endless discussion.

Key Responsibilities

1. Current State Assessment & Pipeline Audit

•    Independently explore, understand, and document the existing data infrastructure, pipeline architecture, and data flows without requiring hand-holding.

•    Identify bottlenecks, redundancies, anti-patterns, and reliability risks across ETL workflows.

•    Deliver a clear, prioritized assessment report within the first two weeks, outlining findings and a recommended action plan.

2. ETL Pipeline Design & Engineering

•    Redesign and rebuild fragile or inefficient pipelines using industry best practices.

•    Develop robust, scalable ETL workflows using Amazon Glue and other relevant tools.

•    Handle data ingestion from diverse sources including relational databases, APIs, flat files, and logs.

•    Write and optimize complex SQL queries to support analytics and reporting requirements.

•    Set up or improve data warehouse and data mart structures aligned with business reporting needs.

3. Monitoring, Reliability & Documentation

•    Implement or enhance monitoring frameworks and automated alerting for pipeline failures and data anomalies.

•    Ensure pipelines are auditable, observable, and maintainable by the internal team post-engagement.

•    Produce clear documentation covering data flows, transformation logic, dependencies, and operational runbooks—so nothing leaves with you.

4. Business Intelligence & Analytics Enablement

•    Translate business processes and KPIs into structured, well-modeled data assets.

•    Build or improve dashboards and BI reports in Power BI or equivalent tools.

•    Proactively identify where data gaps or inconsistencies may be causing incorrect reporting or decision-making, and fix them.

•    Define and standardize metric definitions to ensure consistency across dashboards and teams.

5. AI / ML Exploration & POCs

•    Identify practical AI/ML use cases relevant to the business and independently prototype solutions.

•    Build working proof-of-concepts for areas such as Natural Language to SQL, pipeline anomaly detection, or BI automation.

•    Deliver POCs that are demo-ready and clearly documented, not just theoretical.

Non-Negotiable Expectations

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Proactive Discovery

You should be able to explore our systems, documentation, and data independently. We expect questions when clarification is genuinely needed—not as a substitute for self-directed investigation.

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Bias Toward Delivery

We value working deliverables over long assessments or wait-and-see postures. You should be delivering early, iterating often, and demonstrating progress every week.

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Business-Aware Thinking

You understand that data engineering exists to serve business outcomes. Your decisions on data modeling, pipeline design, and reporting should always connect back to business impact.

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Proactive Communication

Raise blockers early. Share what you’ve found. Suggest improvements before being asked. Do not wait for a task list—build one yourself based on what you discover.

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Leave It Better Than You Found It

Every system you touch should be more reliable, better documented, and easier for the internal team to maintain after you leave.

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Collaborative Independence

Work closely with internal stakeholders and the product team to understand context—but do not depend on them for technical decisions. Own those yourself.

Required Skills & Experience

Core Technical Skills

•    8+ years of hands-on experience in data engineering and ETL development

•    Expert-level proficiency in ETL pipeline design, development, and optimization

•    Hands-on experience with Amazon Glue (mandatory)

•    Advanced SQL: complex queries, performance tuning, window functions, indexing strategies

•    Data warehouse and data mart design: dimensional modeling, star/snowflake schemas

•    Data ingestion from heterogeneous sources: APIs, RDBMS, flat files, streaming, logs

•    Monitoring and alerting setup for data pipelines (failure detection, SLA monitoring)

•    Power BI or equivalent BI tool for dashboard and report development

Architecture & Engineering Mindset

•    Strong data modeling fundamentals and ability to independently design scalable models

•    Experience auditing and refactoring existing pipelines in production environments

•    Familiarity with cloud data platforms (AWS preferred): S3, Redshift, RDS, Lambda

•    Comfortable working in environments where documentation may be sparse or incomplete

AI / ML Exposure (Desirable)

•    Exposure to ML pipelines, feature stores, or data preparation for model training

•    Hands-on experience or experimentation with NLP-to-SQL, LLM-powered analytics, or similar AI-driven BI tools

•    Ability to rapidly prototype and validate AI/ML use cases independently

Soft Skills That Matter for This Role

•    Self-directed: comfortable starting from ambiguity and structuring your own workplan

•    Strong communicator: can explain technical findings clearly to non-technical stakeholders

•    Problem-first thinking: approaches data issues from a business impact lens, not just a technical one

•    High ownership: treats the engagement outcomes as your own, not just billable hours

Candidates who require constant direction need not apply. We are looking for someone who turns ambiguity into action.