job specification

Job Overview

Bitrock is a high‑end consulting and system integration company, strongly committed to offering cutting‑edge and innovative solutions. Our tailored consulting services enable our clients to preserve the value of legacy investments while migrating to more efficient systems and infrastructure. We take a holistic approach to technology: we consider each system in its totality, as a set of interconnected elements that work together to meet business needs.

We thrive on overcoming challenges to help our clients reach their goals, by supporting them in the following areas: Data, AI & ML Engineering;
Back‑end Engineering, Platform Engineering, Front‑end Engineering, Product Design & UX Engineering, Mobile App Development, Quality Assurance, FinOps, Governance. The effectiveness of our solutions also stems from partnerships with key technology vendors, like HashiCorp, Confluent, Lightbend, Databricks, and Meterian.

We’re looking for a freelance Data Engineer to join a small, cross‑functional team focused on internal process optimization, with a strong emphasis on mapping processes to improve through AI.

This is not a heads‑down execution role. The team is lean, so we need someone smart and genuinely flexible—comfortable jumping between hands‑on data engineering, use‑case analysis, scoping, and whatever else needs doing. Strong technical chops are a baseline;
the differentiator is the ability to think broadly and contribute beyond a narrow job description.

• Own the data lifecycle on our platform: ingestion, ETL/ELT, modeling — built on dbt, Redshift, S3 and related AWS services.
• Enable agentic workflows: build features that allow AI‑driven workflows to be deployed on the data platform.
• Support dashboarding & sharing: collaborate with the team building dashboards, improving the tooling and process for publishing and sharing them.
• Help with analysis and beyond: scope use cases, analyze problems, and chip in wherever the team needs an extra smart pair of hands — this part of the role is as important as the engineering one.

• Upper‑mid seniority or above, fully autonomous end‑to‑end.
• Solid hands‑on experience with AWS (S3, Redshift, related services) and dbt.
• Strong command of modern ETL/ELT patterns and data engineering best practices (testing, versioning, pipeline observability).
• Smart, curious, and flexible: comfortable with ambiguity, shifting priorities, and contributing outside a strict “data engineer” lane.
• Strong analytical thinking — you can sit in a use‑case discussion and add value, not just take requirements.