Data Engineer - Freelance

Posted: 19.05.2026

Job reference: 6e7312d7a80b7d0eae392363b698a6c7

Listing type: Basic

Location

Client / Employer: Bitrock

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 optimisation, 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, modelling — 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, analyse 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.